A.C. Viré
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Floating offshore wind turbines (FOWTs) enable access to deep-water sites with strong and consistent wind resources but introduce aero-hydro-servo-elastic complexity through platform motions and mooring dynamics that can affect power production. This thesis investigates how floater type and mooring stiffness influence energy yield using time-domain simulations of the IEA 15 MW reference turbine in OpenFAST. Four floating concepts (WindCrete spar, ActiveFloat and VolturnUS-S semi-submersibles, and a reference tension-leg platform) are evaluated under identical conditions and compared to a fixed reference case.
Free-decay tests are used to determine natural periods and damping, followed by operational simulations to assess platform motions, power variation, and annual energy production (AEP). Results show that floater type strongly affects dynamics, with the TLP exhibiting minimal motion and semi-submersibles the largest surge response. Although power variation increases significantly (4–14×) for floating systems, all concepts achieve AEP within 2% of the fixed turbine. Mooring stiffness mainly influences motion response but has limited impact on mean power.
...
Free-decay tests are used to determine natural periods and damping, followed by operational simulations to assess platform motions, power variation, and annual energy production (AEP). Results show that floater type strongly affects dynamics, with the TLP exhibiting minimal motion and semi-submersibles the largest surge response. Although power variation increases significantly (4–14×) for floating systems, all concepts achieve AEP within 2% of the fixed turbine. Mooring stiffness mainly influences motion response but has limited impact on mean power.
...
Floating offshore wind turbines (FOWTs) enable access to deep-water sites with strong and consistent wind resources but introduce aero-hydro-servo-elastic complexity through platform motions and mooring dynamics that can affect power production. This thesis investigates how floater type and mooring stiffness influence energy yield using time-domain simulations of the IEA 15 MW reference turbine in OpenFAST. Four floating concepts (WindCrete spar, ActiveFloat and VolturnUS-S semi-submersibles, and a reference tension-leg platform) are evaluated under identical conditions and compared to a fixed reference case.
Free-decay tests are used to determine natural periods and damping, followed by operational simulations to assess platform motions, power variation, and annual energy production (AEP). Results show that floater type strongly affects dynamics, with the TLP exhibiting minimal motion and semi-submersibles the largest surge response. Although power variation increases significantly (4–14×) for floating systems, all concepts achieve AEP within 2% of the fixed turbine. Mooring stiffness mainly influences motion response but has limited impact on mean power.
Free-decay tests are used to determine natural periods and damping, followed by operational simulations to assess platform motions, power variation, and annual energy production (AEP). Results show that floater type strongly affects dynamics, with the TLP exhibiting minimal motion and semi-submersibles the largest surge response. Although power variation increases significantly (4–14×) for floating systems, all concepts achieve AEP within 2% of the fixed turbine. Mooring stiffness mainly influences motion response but has limited impact on mean power.
As nations broaden their renewable energy portfolios, wind power is playing an increasingly important role in the energy landscape. Although onshore wind continues to dominate the wind energy mix in the EU, rising energy demand and ambitious climate targets have driven the development of offshore wind farms and spurred research into floating offshore wind turbines.
This thesis began with the goal of developing a data-driven modeling approach that maps site conditions to the load statistics on floating offshore wind turbines. The motivation for this research emerged from a need to accelerate the site-selection process, include more site variables, and achieve accurate load estimates while keeping the computational expense low.
Before installation, all modern wind turbines must be carefully assessed for structural loads to ensure safety and performance throughout their lifetimes. Floating turbines, in particular, introduce more complexity—more environmental variables to consider, and higher uncertainty in the dynamic response. This added complexity makes analyzing structural loads expensive and time-consuming, often more so than for fixed-bottom counterparts, and it makes understanding the behavior of floating wind turbines even more important. The process of fatigue damage calculation, in particular, is computationally intensive, often requiring thousands of costly simulations. Because it is not feasible to run simulations for every possible sea state, engineers typically reduce the problem by selecting a set of variables and binning or lumping sea states to limit the number of required simulations. However, for floating wind turbines, the choice of which variables to include and how to perform this binning remains an open question. This motivates the use of reliable data-driven surrogate models that can make quick estimates of loads on wind turbines while maintaining high accuracy.
Although most existing work relies on deterministic surrogate models, offshore wind environments exhibit strong stochasticity, making turbine loads inherently uncertain. This motivates the need for uncertainty quantification to characterize not only expected loads but also estimate their conditional variability. This dissertation, therefore, develops a probabilistic data-driven methodology that propagates environmental uncertainty to the 10-minute damage equivalent loads of onshore, offshore, and floating offshore wind turbines.
Several deterministic and probabilistic data-driven models are benchmarked on onshore and fixed-bottom offshore wind turbines. The evaluation consists not only of judging the accuracy of a model, but also of practical aspects such as the robustness of the model, sensitivity to hyperparameters, and ease of implementation. Compared to deterministic approaches, which require multiple seed repetitions prior to training, it is demonstrated that with probabilistic models, this step may not be necessary to achieve high accuracy predictions (𝑅2 > 0.95), thereby saving precious computational resources needed to generate the training database. Widely used Gaussian process regression is shown to accurately estimate the conditional mean of the response with a relatively small training dataset of Q102 - 103) samples. Wasserstein-conditional generative adversarial network is used as one of the probabilistic regression models. Despite learning the functional mapping with errors comparable to the best-performing models, it is found to be very complex to implement and requires extensive hyperparameter tuning. Simpler 4th-degree polynomials are shown to make good predictions in the onshore case, but are prone to overfitting and susceptible to the additional noise introduced by the hydrodynamic features. Overall, mixture density networks are shown to provide the best combination of consistency, accuracy, and practical ease of use.
Based on this analysis, the study extends the use of mixture density networks to a more sophisticated application of spar-type floating offshore wind turbine. It is shown to successfully capture the conditional response in terms of the normalized 2-Wasserstein distance despite the added complexity. The surrogate is further used to make probabilistic estimates of the lifetime damage equivalent loads on four potential floating wind turbine sites. Since the surrogate model is fast (order of milliseconds once trained), load predictions can be made over all sea states within seconds, without the need to lump or bin the sea states beforehand. The uncertainty in the aggregated lifetime fatigue loads due to stochastic inputs is extremely narrow, with variability on the order of only 0.1–0.5% of their mean values. This results from summing the 10-minute damage equivalent loads over a million occurrences, effectively nullifying the impact of the outliers. The use of a probabilistic surrogate that correctly captures the conditional distribution is still useful, as it minimizes the aggregation of error in the final response.
Through these analyses, it is demonstrated that surrogate models can be powerful tools for fatigue estimation in the site analysis process, especially for floating wind turbines, where the choice of variables and binning methods is still an open question. Additionally, using probabilistic surrogates like mixture density networks helps reduce bias in calculating the aggregate mean fatigue, as the conditional distributions are heteroscedastic and not always normally distributed.
...
This thesis began with the goal of developing a data-driven modeling approach that maps site conditions to the load statistics on floating offshore wind turbines. The motivation for this research emerged from a need to accelerate the site-selection process, include more site variables, and achieve accurate load estimates while keeping the computational expense low.
Before installation, all modern wind turbines must be carefully assessed for structural loads to ensure safety and performance throughout their lifetimes. Floating turbines, in particular, introduce more complexity—more environmental variables to consider, and higher uncertainty in the dynamic response. This added complexity makes analyzing structural loads expensive and time-consuming, often more so than for fixed-bottom counterparts, and it makes understanding the behavior of floating wind turbines even more important. The process of fatigue damage calculation, in particular, is computationally intensive, often requiring thousands of costly simulations. Because it is not feasible to run simulations for every possible sea state, engineers typically reduce the problem by selecting a set of variables and binning or lumping sea states to limit the number of required simulations. However, for floating wind turbines, the choice of which variables to include and how to perform this binning remains an open question. This motivates the use of reliable data-driven surrogate models that can make quick estimates of loads on wind turbines while maintaining high accuracy.
Although most existing work relies on deterministic surrogate models, offshore wind environments exhibit strong stochasticity, making turbine loads inherently uncertain. This motivates the need for uncertainty quantification to characterize not only expected loads but also estimate their conditional variability. This dissertation, therefore, develops a probabilistic data-driven methodology that propagates environmental uncertainty to the 10-minute damage equivalent loads of onshore, offshore, and floating offshore wind turbines.
Several deterministic and probabilistic data-driven models are benchmarked on onshore and fixed-bottom offshore wind turbines. The evaluation consists not only of judging the accuracy of a model, but also of practical aspects such as the robustness of the model, sensitivity to hyperparameters, and ease of implementation. Compared to deterministic approaches, which require multiple seed repetitions prior to training, it is demonstrated that with probabilistic models, this step may not be necessary to achieve high accuracy predictions (𝑅2 > 0.95), thereby saving precious computational resources needed to generate the training database. Widely used Gaussian process regression is shown to accurately estimate the conditional mean of the response with a relatively small training dataset of Q102 - 103) samples. Wasserstein-conditional generative adversarial network is used as one of the probabilistic regression models. Despite learning the functional mapping with errors comparable to the best-performing models, it is found to be very complex to implement and requires extensive hyperparameter tuning. Simpler 4th-degree polynomials are shown to make good predictions in the onshore case, but are prone to overfitting and susceptible to the additional noise introduced by the hydrodynamic features. Overall, mixture density networks are shown to provide the best combination of consistency, accuracy, and practical ease of use.
Based on this analysis, the study extends the use of mixture density networks to a more sophisticated application of spar-type floating offshore wind turbine. It is shown to successfully capture the conditional response in terms of the normalized 2-Wasserstein distance despite the added complexity. The surrogate is further used to make probabilistic estimates of the lifetime damage equivalent loads on four potential floating wind turbine sites. Since the surrogate model is fast (order of milliseconds once trained), load predictions can be made over all sea states within seconds, without the need to lump or bin the sea states beforehand. The uncertainty in the aggregated lifetime fatigue loads due to stochastic inputs is extremely narrow, with variability on the order of only 0.1–0.5% of their mean values. This results from summing the 10-minute damage equivalent loads over a million occurrences, effectively nullifying the impact of the outliers. The use of a probabilistic surrogate that correctly captures the conditional distribution is still useful, as it minimizes the aggregation of error in the final response.
Through these analyses, it is demonstrated that surrogate models can be powerful tools for fatigue estimation in the site analysis process, especially for floating wind turbines, where the choice of variables and binning methods is still an open question. Additionally, using probabilistic surrogates like mixture density networks helps reduce bias in calculating the aggregate mean fatigue, as the conditional distributions are heteroscedastic and not always normally distributed.
...
As nations broaden their renewable energy portfolios, wind power is playing an increasingly important role in the energy landscape. Although onshore wind continues to dominate the wind energy mix in the EU, rising energy demand and ambitious climate targets have driven the development of offshore wind farms and spurred research into floating offshore wind turbines.
This thesis began with the goal of developing a data-driven modeling approach that maps site conditions to the load statistics on floating offshore wind turbines. The motivation for this research emerged from a need to accelerate the site-selection process, include more site variables, and achieve accurate load estimates while keeping the computational expense low.
Before installation, all modern wind turbines must be carefully assessed for structural loads to ensure safety and performance throughout their lifetimes. Floating turbines, in particular, introduce more complexity—more environmental variables to consider, and higher uncertainty in the dynamic response. This added complexity makes analyzing structural loads expensive and time-consuming, often more so than for fixed-bottom counterparts, and it makes understanding the behavior of floating wind turbines even more important. The process of fatigue damage calculation, in particular, is computationally intensive, often requiring thousands of costly simulations. Because it is not feasible to run simulations for every possible sea state, engineers typically reduce the problem by selecting a set of variables and binning or lumping sea states to limit the number of required simulations. However, for floating wind turbines, the choice of which variables to include and how to perform this binning remains an open question. This motivates the use of reliable data-driven surrogate models that can make quick estimates of loads on wind turbines while maintaining high accuracy.
Although most existing work relies on deterministic surrogate models, offshore wind environments exhibit strong stochasticity, making turbine loads inherently uncertain. This motivates the need for uncertainty quantification to characterize not only expected loads but also estimate their conditional variability. This dissertation, therefore, develops a probabilistic data-driven methodology that propagates environmental uncertainty to the 10-minute damage equivalent loads of onshore, offshore, and floating offshore wind turbines.
Several deterministic and probabilistic data-driven models are benchmarked on onshore and fixed-bottom offshore wind turbines. The evaluation consists not only of judging the accuracy of a model, but also of practical aspects such as the robustness of the model, sensitivity to hyperparameters, and ease of implementation. Compared to deterministic approaches, which require multiple seed repetitions prior to training, it is demonstrated that with probabilistic models, this step may not be necessary to achieve high accuracy predictions (𝑅2 > 0.95), thereby saving precious computational resources needed to generate the training database. Widely used Gaussian process regression is shown to accurately estimate the conditional mean of the response with a relatively small training dataset of Q102 - 103) samples. Wasserstein-conditional generative adversarial network is used as one of the probabilistic regression models. Despite learning the functional mapping with errors comparable to the best-performing models, it is found to be very complex to implement and requires extensive hyperparameter tuning. Simpler 4th-degree polynomials are shown to make good predictions in the onshore case, but are prone to overfitting and susceptible to the additional noise introduced by the hydrodynamic features. Overall, mixture density networks are shown to provide the best combination of consistency, accuracy, and practical ease of use.
Based on this analysis, the study extends the use of mixture density networks to a more sophisticated application of spar-type floating offshore wind turbine. It is shown to successfully capture the conditional response in terms of the normalized 2-Wasserstein distance despite the added complexity. The surrogate is further used to make probabilistic estimates of the lifetime damage equivalent loads on four potential floating wind turbine sites. Since the surrogate model is fast (order of milliseconds once trained), load predictions can be made over all sea states within seconds, without the need to lump or bin the sea states beforehand. The uncertainty in the aggregated lifetime fatigue loads due to stochastic inputs is extremely narrow, with variability on the order of only 0.1–0.5% of their mean values. This results from summing the 10-minute damage equivalent loads over a million occurrences, effectively nullifying the impact of the outliers. The use of a probabilistic surrogate that correctly captures the conditional distribution is still useful, as it minimizes the aggregation of error in the final response.
Through these analyses, it is demonstrated that surrogate models can be powerful tools for fatigue estimation in the site analysis process, especially for floating wind turbines, where the choice of variables and binning methods is still an open question. Additionally, using probabilistic surrogates like mixture density networks helps reduce bias in calculating the aggregate mean fatigue, as the conditional distributions are heteroscedastic and not always normally distributed.
This thesis began with the goal of developing a data-driven modeling approach that maps site conditions to the load statistics on floating offshore wind turbines. The motivation for this research emerged from a need to accelerate the site-selection process, include more site variables, and achieve accurate load estimates while keeping the computational expense low.
Before installation, all modern wind turbines must be carefully assessed for structural loads to ensure safety and performance throughout their lifetimes. Floating turbines, in particular, introduce more complexity—more environmental variables to consider, and higher uncertainty in the dynamic response. This added complexity makes analyzing structural loads expensive and time-consuming, often more so than for fixed-bottom counterparts, and it makes understanding the behavior of floating wind turbines even more important. The process of fatigue damage calculation, in particular, is computationally intensive, often requiring thousands of costly simulations. Because it is not feasible to run simulations for every possible sea state, engineers typically reduce the problem by selecting a set of variables and binning or lumping sea states to limit the number of required simulations. However, for floating wind turbines, the choice of which variables to include and how to perform this binning remains an open question. This motivates the use of reliable data-driven surrogate models that can make quick estimates of loads on wind turbines while maintaining high accuracy.
Although most existing work relies on deterministic surrogate models, offshore wind environments exhibit strong stochasticity, making turbine loads inherently uncertain. This motivates the need for uncertainty quantification to characterize not only expected loads but also estimate their conditional variability. This dissertation, therefore, develops a probabilistic data-driven methodology that propagates environmental uncertainty to the 10-minute damage equivalent loads of onshore, offshore, and floating offshore wind turbines.
Several deterministic and probabilistic data-driven models are benchmarked on onshore and fixed-bottom offshore wind turbines. The evaluation consists not only of judging the accuracy of a model, but also of practical aspects such as the robustness of the model, sensitivity to hyperparameters, and ease of implementation. Compared to deterministic approaches, which require multiple seed repetitions prior to training, it is demonstrated that with probabilistic models, this step may not be necessary to achieve high accuracy predictions (𝑅2 > 0.95), thereby saving precious computational resources needed to generate the training database. Widely used Gaussian process regression is shown to accurately estimate the conditional mean of the response with a relatively small training dataset of Q102 - 103) samples. Wasserstein-conditional generative adversarial network is used as one of the probabilistic regression models. Despite learning the functional mapping with errors comparable to the best-performing models, it is found to be very complex to implement and requires extensive hyperparameter tuning. Simpler 4th-degree polynomials are shown to make good predictions in the onshore case, but are prone to overfitting and susceptible to the additional noise introduced by the hydrodynamic features. Overall, mixture density networks are shown to provide the best combination of consistency, accuracy, and practical ease of use.
Based on this analysis, the study extends the use of mixture density networks to a more sophisticated application of spar-type floating offshore wind turbine. It is shown to successfully capture the conditional response in terms of the normalized 2-Wasserstein distance despite the added complexity. The surrogate is further used to make probabilistic estimates of the lifetime damage equivalent loads on four potential floating wind turbine sites. Since the surrogate model is fast (order of milliseconds once trained), load predictions can be made over all sea states within seconds, without the need to lump or bin the sea states beforehand. The uncertainty in the aggregated lifetime fatigue loads due to stochastic inputs is extremely narrow, with variability on the order of only 0.1–0.5% of their mean values. This results from summing the 10-minute damage equivalent loads over a million occurrences, effectively nullifying the impact of the outliers. The use of a probabilistic surrogate that correctly captures the conditional distribution is still useful, as it minimizes the aggregation of error in the final response.
Through these analyses, it is demonstrated that surrogate models can be powerful tools for fatigue estimation in the site analysis process, especially for floating wind turbines, where the choice of variables and binning methods is still an open question. Additionally, using probabilistic surrogates like mixture density networks helps reduce bias in calculating the aggregate mean fatigue, as the conditional distributions are heteroscedastic and not always normally distributed.
Offshore wind farms are central to the global energy transition, especially as larger turbines and new substructures enable deployment at greater depths. While cost and energy yield are typically the main drivers of design, integrating environmental impacts into the design process is becoming increasingly important. This thesis develops a framework in which life cycle greenhouse gas (GHG) emissions are quantified and expressed through the global warming potential (GWP) in grams of CO₂-equivalent per kWh, thereby creating a metric that can be directly compared with the levelized cost of energy (LCOE). In doing so, GWP becomes a design-relevant parameter for optimizing offshore wind farms not only for economics, but also for sustainability.
The study investigates wind farms ranging from 400 MW to 1000 MW, with turbine ratings of 5 MW, 10 MW, 15 MW, and 22 MW. Three foundation types are considered: monopile (fixed-bottom), semi-submersible (floating), and spar buoy (floating). A bottom-up life cycle assessment (LCA) was applied, covering material production, manufacturing, transport, installation, operation, and decommissioning. The assessment incorporates a logistics model for marine operations and farm-level scaling to ensure realistic representation of installation demands and component quantities. Results were normalized by the annual energy production (AEP) of each configuration, enabling fair comparison across technologies and site conditions.
Findings show that increasing turbine size consistently reduces GWP, as fewer units are needed to achieve a given farm capacity, lowering both material requirements and installation activities. In a 400 MW farm, semi-submersible turbines achieve GWP reductions of 50.50%, 65.28%, and 73.96% when scaling from 5 MW to 10 MW, 15 MW, and 22 MW, respectively. Spar buoy turbines show similar reductions of 50.91%, 67.29%, and 75.93%. For monopile foundations, also considered at shallower depths, larger turbines likewise reduce environmental impacts per kWh. Expanding farm capacity from 400 MW to 1000 MW further enhances performance, as economies of scale amplify both environmental and economic benefits.
Economic analysis reveals parallel trends. LCOE decreases with turbine scaling for all foundation types, although monopiles remain most competitive in shallow waters, while spar buoys gain advantage in deeper sites. Semi-submersibles bridge the intermediate depths but also benefit significantly from turbine upscaling. The alignment between GWP and LCOE trends indicates that design decisions favoring cost reductions also contribute to environmental sustainability. However, the marginal reduction in GWP diminishes at very high turbine ratings, suggesting that beyond a threshold, other factors such as LCOE, reliability and system integration may dominate design trade-offs.
In conclusion, this thesis demonstrates that by quantifying environmental impacts into a comparable metric, sustainability can be incorporated into wind farm design. Across farm scales, turbine ratings, and foundation types, results consistently show that larger turbines on monopile, semi-submersible, and spar foundations deliver lower GWP and improved LCOE, confirming that technology scaling serves both economic and environmental objectives in offshore wind development. ...
The study investigates wind farms ranging from 400 MW to 1000 MW, with turbine ratings of 5 MW, 10 MW, 15 MW, and 22 MW. Three foundation types are considered: monopile (fixed-bottom), semi-submersible (floating), and spar buoy (floating). A bottom-up life cycle assessment (LCA) was applied, covering material production, manufacturing, transport, installation, operation, and decommissioning. The assessment incorporates a logistics model for marine operations and farm-level scaling to ensure realistic representation of installation demands and component quantities. Results were normalized by the annual energy production (AEP) of each configuration, enabling fair comparison across technologies and site conditions.
Findings show that increasing turbine size consistently reduces GWP, as fewer units are needed to achieve a given farm capacity, lowering both material requirements and installation activities. In a 400 MW farm, semi-submersible turbines achieve GWP reductions of 50.50%, 65.28%, and 73.96% when scaling from 5 MW to 10 MW, 15 MW, and 22 MW, respectively. Spar buoy turbines show similar reductions of 50.91%, 67.29%, and 75.93%. For monopile foundations, also considered at shallower depths, larger turbines likewise reduce environmental impacts per kWh. Expanding farm capacity from 400 MW to 1000 MW further enhances performance, as economies of scale amplify both environmental and economic benefits.
Economic analysis reveals parallel trends. LCOE decreases with turbine scaling for all foundation types, although monopiles remain most competitive in shallow waters, while spar buoys gain advantage in deeper sites. Semi-submersibles bridge the intermediate depths but also benefit significantly from turbine upscaling. The alignment between GWP and LCOE trends indicates that design decisions favoring cost reductions also contribute to environmental sustainability. However, the marginal reduction in GWP diminishes at very high turbine ratings, suggesting that beyond a threshold, other factors such as LCOE, reliability and system integration may dominate design trade-offs.
In conclusion, this thesis demonstrates that by quantifying environmental impacts into a comparable metric, sustainability can be incorporated into wind farm design. Across farm scales, turbine ratings, and foundation types, results consistently show that larger turbines on monopile, semi-submersible, and spar foundations deliver lower GWP and improved LCOE, confirming that technology scaling serves both economic and environmental objectives in offshore wind development. ...
Offshore wind farms are central to the global energy transition, especially as larger turbines and new substructures enable deployment at greater depths. While cost and energy yield are typically the main drivers of design, integrating environmental impacts into the design process is becoming increasingly important. This thesis develops a framework in which life cycle greenhouse gas (GHG) emissions are quantified and expressed through the global warming potential (GWP) in grams of CO₂-equivalent per kWh, thereby creating a metric that can be directly compared with the levelized cost of energy (LCOE). In doing so, GWP becomes a design-relevant parameter for optimizing offshore wind farms not only for economics, but also for sustainability.
The study investigates wind farms ranging from 400 MW to 1000 MW, with turbine ratings of 5 MW, 10 MW, 15 MW, and 22 MW. Three foundation types are considered: monopile (fixed-bottom), semi-submersible (floating), and spar buoy (floating). A bottom-up life cycle assessment (LCA) was applied, covering material production, manufacturing, transport, installation, operation, and decommissioning. The assessment incorporates a logistics model for marine operations and farm-level scaling to ensure realistic representation of installation demands and component quantities. Results were normalized by the annual energy production (AEP) of each configuration, enabling fair comparison across technologies and site conditions.
Findings show that increasing turbine size consistently reduces GWP, as fewer units are needed to achieve a given farm capacity, lowering both material requirements and installation activities. In a 400 MW farm, semi-submersible turbines achieve GWP reductions of 50.50%, 65.28%, and 73.96% when scaling from 5 MW to 10 MW, 15 MW, and 22 MW, respectively. Spar buoy turbines show similar reductions of 50.91%, 67.29%, and 75.93%. For monopile foundations, also considered at shallower depths, larger turbines likewise reduce environmental impacts per kWh. Expanding farm capacity from 400 MW to 1000 MW further enhances performance, as economies of scale amplify both environmental and economic benefits.
Economic analysis reveals parallel trends. LCOE decreases with turbine scaling for all foundation types, although monopiles remain most competitive in shallow waters, while spar buoys gain advantage in deeper sites. Semi-submersibles bridge the intermediate depths but also benefit significantly from turbine upscaling. The alignment between GWP and LCOE trends indicates that design decisions favoring cost reductions also contribute to environmental sustainability. However, the marginal reduction in GWP diminishes at very high turbine ratings, suggesting that beyond a threshold, other factors such as LCOE, reliability and system integration may dominate design trade-offs.
In conclusion, this thesis demonstrates that by quantifying environmental impacts into a comparable metric, sustainability can be incorporated into wind farm design. Across farm scales, turbine ratings, and foundation types, results consistently show that larger turbines on monopile, semi-submersible, and spar foundations deliver lower GWP and improved LCOE, confirming that technology scaling serves both economic and environmental objectives in offshore wind development.
The study investigates wind farms ranging from 400 MW to 1000 MW, with turbine ratings of 5 MW, 10 MW, 15 MW, and 22 MW. Three foundation types are considered: monopile (fixed-bottom), semi-submersible (floating), and spar buoy (floating). A bottom-up life cycle assessment (LCA) was applied, covering material production, manufacturing, transport, installation, operation, and decommissioning. The assessment incorporates a logistics model for marine operations and farm-level scaling to ensure realistic representation of installation demands and component quantities. Results were normalized by the annual energy production (AEP) of each configuration, enabling fair comparison across technologies and site conditions.
Findings show that increasing turbine size consistently reduces GWP, as fewer units are needed to achieve a given farm capacity, lowering both material requirements and installation activities. In a 400 MW farm, semi-submersible turbines achieve GWP reductions of 50.50%, 65.28%, and 73.96% when scaling from 5 MW to 10 MW, 15 MW, and 22 MW, respectively. Spar buoy turbines show similar reductions of 50.91%, 67.29%, and 75.93%. For monopile foundations, also considered at shallower depths, larger turbines likewise reduce environmental impacts per kWh. Expanding farm capacity from 400 MW to 1000 MW further enhances performance, as economies of scale amplify both environmental and economic benefits.
Economic analysis reveals parallel trends. LCOE decreases with turbine scaling for all foundation types, although monopiles remain most competitive in shallow waters, while spar buoys gain advantage in deeper sites. Semi-submersibles bridge the intermediate depths but also benefit significantly from turbine upscaling. The alignment between GWP and LCOE trends indicates that design decisions favoring cost reductions also contribute to environmental sustainability. However, the marginal reduction in GWP diminishes at very high turbine ratings, suggesting that beyond a threshold, other factors such as LCOE, reliability and system integration may dominate design trade-offs.
In conclusion, this thesis demonstrates that by quantifying environmental impacts into a comparable metric, sustainability can be incorporated into wind farm design. Across farm scales, turbine ratings, and foundation types, results consistently show that larger turbines on monopile, semi-submersible, and spar foundations deliver lower GWP and improved LCOE, confirming that technology scaling serves both economic and environmental objectives in offshore wind development.
Floating offshore wind turbines enable renewable energy expansion into deep-water regions with stronger, more consistent winds. However, they are subject to continuous platform motion from wind and waves, which complicates blade loading and wake aerodynamics. While surge and pitch effects have been extensively studied, the aerodynamic influence of roll motion remains underexplored. Roll motion uniquely induces a non-uniform tangential velocity field across the rotor, which would have implications on the blade loading, structure fatigue and the wake aerodynamics.
This thesis presents a baseline analysis of blade loading and near-wake aerodynamics for a FOWT under prescribed roll motions. A coupled high-fidelity GPU-based Large-Eddy Simulation code GRASP resolved turbulent wake dynamics, while OpenFAST calculated blade loads. Coupling was achieved via the Filtered Actuator Line Method and the AspFAST application programming interface. The IEA 15MW reference turbine was modeled under steady, uniform inflow, with variations in roll amplitude (5° and 10°), roll frequency (0.03Hz and 0.05Hz), and tip-speed ratio (TSR 7 and TSR 9) in a full factorial test set.
Results show that roll motion leads to asymmetric fluctuations across the rotor plane. Normal forces display strong vertical asymmetry, with greater variations in the upper side of the rotor due to its larger distance from the roll center. A kinematic analysis deriving the variation of tangential velocity both azimuthally and over time supported this finding. Tangential forces exhibit lateral variation, with a left-right asymmetry across the rotor plane, though the magnitude of variation is similar on both sides. Both roll amplitude and frequency increase the magnitude of loading fluctuations, while TSR influences their blade spanwise distribution.
Power Spectral Density analysis reveals that roll introduces spectral content at the roll frequency and at 1P sideband frequencies, the latter arising from modulation of the 1P frequency by roll. Higher roll amplitudes, frequencies, and TSRs amplify these load fluctuations. While the prominence of the 1P frequency is negligible at the blade, it is strong in the near wake along with the roll frequency, 3P frequency, and 3P sidebands.
The unsteady blade loads directly shape the near wake. The stable helical vortex system of a bottom-fixed turbine is replaced by an oscillatory corkscrew structure that breaks down earlier and more chaotically. Increased roll amplitude causes stronger lateral oscillations and fragmented vortices, while higher roll frequency produces shorter wavelength perturbations, accelerating vortex pairing. Higher TSR strengthens the initial vortices and accelerates breakdown. Standard deviation of velocity fields in the wake confirms larger fluctuations at the top of the rotor due to its greater distance from the roll axis.
This study establishes a direct link between roll-induced blade loads and near-wake dynamics, providing a foundation for improved FOWT design, control, and wake modeling. Future work should assess far-wake impacts, the persistence of lateral velocities induced by rotational motion, and inertial effects under highly unsteady roll conditions. ...
This thesis presents a baseline analysis of blade loading and near-wake aerodynamics for a FOWT under prescribed roll motions. A coupled high-fidelity GPU-based Large-Eddy Simulation code GRASP resolved turbulent wake dynamics, while OpenFAST calculated blade loads. Coupling was achieved via the Filtered Actuator Line Method and the AspFAST application programming interface. The IEA 15MW reference turbine was modeled under steady, uniform inflow, with variations in roll amplitude (5° and 10°), roll frequency (0.03Hz and 0.05Hz), and tip-speed ratio (TSR 7 and TSR 9) in a full factorial test set.
Results show that roll motion leads to asymmetric fluctuations across the rotor plane. Normal forces display strong vertical asymmetry, with greater variations in the upper side of the rotor due to its larger distance from the roll center. A kinematic analysis deriving the variation of tangential velocity both azimuthally and over time supported this finding. Tangential forces exhibit lateral variation, with a left-right asymmetry across the rotor plane, though the magnitude of variation is similar on both sides. Both roll amplitude and frequency increase the magnitude of loading fluctuations, while TSR influences their blade spanwise distribution.
Power Spectral Density analysis reveals that roll introduces spectral content at the roll frequency and at 1P sideband frequencies, the latter arising from modulation of the 1P frequency by roll. Higher roll amplitudes, frequencies, and TSRs amplify these load fluctuations. While the prominence of the 1P frequency is negligible at the blade, it is strong in the near wake along with the roll frequency, 3P frequency, and 3P sidebands.
The unsteady blade loads directly shape the near wake. The stable helical vortex system of a bottom-fixed turbine is replaced by an oscillatory corkscrew structure that breaks down earlier and more chaotically. Increased roll amplitude causes stronger lateral oscillations and fragmented vortices, while higher roll frequency produces shorter wavelength perturbations, accelerating vortex pairing. Higher TSR strengthens the initial vortices and accelerates breakdown. Standard deviation of velocity fields in the wake confirms larger fluctuations at the top of the rotor due to its greater distance from the roll axis.
This study establishes a direct link between roll-induced blade loads and near-wake dynamics, providing a foundation for improved FOWT design, control, and wake modeling. Future work should assess far-wake impacts, the persistence of lateral velocities induced by rotational motion, and inertial effects under highly unsteady roll conditions. ...
Floating offshore wind turbines enable renewable energy expansion into deep-water regions with stronger, more consistent winds. However, they are subject to continuous platform motion from wind and waves, which complicates blade loading and wake aerodynamics. While surge and pitch effects have been extensively studied, the aerodynamic influence of roll motion remains underexplored. Roll motion uniquely induces a non-uniform tangential velocity field across the rotor, which would have implications on the blade loading, structure fatigue and the wake aerodynamics.
This thesis presents a baseline analysis of blade loading and near-wake aerodynamics for a FOWT under prescribed roll motions. A coupled high-fidelity GPU-based Large-Eddy Simulation code GRASP resolved turbulent wake dynamics, while OpenFAST calculated blade loads. Coupling was achieved via the Filtered Actuator Line Method and the AspFAST application programming interface. The IEA 15MW reference turbine was modeled under steady, uniform inflow, with variations in roll amplitude (5° and 10°), roll frequency (0.03Hz and 0.05Hz), and tip-speed ratio (TSR 7 and TSR 9) in a full factorial test set.
Results show that roll motion leads to asymmetric fluctuations across the rotor plane. Normal forces display strong vertical asymmetry, with greater variations in the upper side of the rotor due to its larger distance from the roll center. A kinematic analysis deriving the variation of tangential velocity both azimuthally and over time supported this finding. Tangential forces exhibit lateral variation, with a left-right asymmetry across the rotor plane, though the magnitude of variation is similar on both sides. Both roll amplitude and frequency increase the magnitude of loading fluctuations, while TSR influences their blade spanwise distribution.
Power Spectral Density analysis reveals that roll introduces spectral content at the roll frequency and at 1P sideband frequencies, the latter arising from modulation of the 1P frequency by roll. Higher roll amplitudes, frequencies, and TSRs amplify these load fluctuations. While the prominence of the 1P frequency is negligible at the blade, it is strong in the near wake along with the roll frequency, 3P frequency, and 3P sidebands.
The unsteady blade loads directly shape the near wake. The stable helical vortex system of a bottom-fixed turbine is replaced by an oscillatory corkscrew structure that breaks down earlier and more chaotically. Increased roll amplitude causes stronger lateral oscillations and fragmented vortices, while higher roll frequency produces shorter wavelength perturbations, accelerating vortex pairing. Higher TSR strengthens the initial vortices and accelerates breakdown. Standard deviation of velocity fields in the wake confirms larger fluctuations at the top of the rotor due to its greater distance from the roll axis.
This study establishes a direct link between roll-induced blade loads and near-wake dynamics, providing a foundation for improved FOWT design, control, and wake modeling. Future work should assess far-wake impacts, the persistence of lateral velocities induced by rotational motion, and inertial effects under highly unsteady roll conditions.
This thesis presents a baseline analysis of blade loading and near-wake aerodynamics for a FOWT under prescribed roll motions. A coupled high-fidelity GPU-based Large-Eddy Simulation code GRASP resolved turbulent wake dynamics, while OpenFAST calculated blade loads. Coupling was achieved via the Filtered Actuator Line Method and the AspFAST application programming interface. The IEA 15MW reference turbine was modeled under steady, uniform inflow, with variations in roll amplitude (5° and 10°), roll frequency (0.03Hz and 0.05Hz), and tip-speed ratio (TSR 7 and TSR 9) in a full factorial test set.
Results show that roll motion leads to asymmetric fluctuations across the rotor plane. Normal forces display strong vertical asymmetry, with greater variations in the upper side of the rotor due to its larger distance from the roll center. A kinematic analysis deriving the variation of tangential velocity both azimuthally and over time supported this finding. Tangential forces exhibit lateral variation, with a left-right asymmetry across the rotor plane, though the magnitude of variation is similar on both sides. Both roll amplitude and frequency increase the magnitude of loading fluctuations, while TSR influences their blade spanwise distribution.
Power Spectral Density analysis reveals that roll introduces spectral content at the roll frequency and at 1P sideband frequencies, the latter arising from modulation of the 1P frequency by roll. Higher roll amplitudes, frequencies, and TSRs amplify these load fluctuations. While the prominence of the 1P frequency is negligible at the blade, it is strong in the near wake along with the roll frequency, 3P frequency, and 3P sidebands.
The unsteady blade loads directly shape the near wake. The stable helical vortex system of a bottom-fixed turbine is replaced by an oscillatory corkscrew structure that breaks down earlier and more chaotically. Increased roll amplitude causes stronger lateral oscillations and fragmented vortices, while higher roll frequency produces shorter wavelength perturbations, accelerating vortex pairing. Higher TSR strengthens the initial vortices and accelerates breakdown. Standard deviation of velocity fields in the wake confirms larger fluctuations at the top of the rotor due to its greater distance from the roll axis.
This study establishes a direct link between roll-induced blade loads and near-wake dynamics, providing a foundation for improved FOWT design, control, and wake modeling. Future work should assess far-wake impacts, the persistence of lateral velocities induced by rotational motion, and inertial effects under highly unsteady roll conditions.
Master thesis
(2025)
-
A. Guntner, A.C. Viré, D.A.M. De Tavernier, Hamid Sarlak, S.J. Watson, Mahdi Abkar
The growing global demand for green energy requires the development of innovative solutions. Floating offshore wind turbines (FOWTs) present significant potential by enabling the harvesting of wind in deep-sea waters. However, as this technology is still in its early stages, the impact of the added six degrees of freedom (6-DOF) motion on wake dynamics and power performance remains unclear. Understanding these effects is critical for the design of floating wind farms. In this work, large eddy simulations and an actuator line model are employed to investigate the wake behavior and power output of the IEA-15MW reference turbine mounted on several floating platform concepts. To explore wake interactions in wind farm scenarios, two tandem FOWTs are modelled. The simulations are carried out in AMR-Wind, coupled with OpenFAST for the platform and blades' motion.
Initial cases are conducted with a laminar inflow to isolate fundamental wake mechanisms. Then, a neutral atmospheric boundary layer (ABL) is recreated to perform investigations with realistic offshore conditions.
Platform motions are found to induce velocity fluctuations in the wake and promote vortex-pairing. High-frequency and large-amplitude motions particularly enhance wake recovery in the upstream wake, although this effect is less pronounced in the downstream wake. Among all 6-DOF, surge is identified as the main driver of wake dynamics.
FOWTs generate larger turbulence levels than their fixed counterparts during laminar inflow. Notably, when the downstream turbine moves in phase with the incoming wake, the resulting interaction leads to the superimposition of the wake structures, forming large, separated low-speed regions.
In contrast, under turbulent conditions, FOWTs generate lower turbulence in the near wake, but similar levels further downstream. In these cases, the differences in mean wake velocity between floating and fixed turbines decrease significantly with distance.
The tandem FOWTs are found to produce less total power when operating at rated speed during neutral ABL conditions. However, the downstream FOWTs exhibit power gains of up to 20\%, indicating that larger floating wind farms are likely to generate more electricity than traditional bottom-fixed offshore wind farms. ...
Initial cases are conducted with a laminar inflow to isolate fundamental wake mechanisms. Then, a neutral atmospheric boundary layer (ABL) is recreated to perform investigations with realistic offshore conditions.
Platform motions are found to induce velocity fluctuations in the wake and promote vortex-pairing. High-frequency and large-amplitude motions particularly enhance wake recovery in the upstream wake, although this effect is less pronounced in the downstream wake. Among all 6-DOF, surge is identified as the main driver of wake dynamics.
FOWTs generate larger turbulence levels than their fixed counterparts during laminar inflow. Notably, when the downstream turbine moves in phase with the incoming wake, the resulting interaction leads to the superimposition of the wake structures, forming large, separated low-speed regions.
In contrast, under turbulent conditions, FOWTs generate lower turbulence in the near wake, but similar levels further downstream. In these cases, the differences in mean wake velocity between floating and fixed turbines decrease significantly with distance.
The tandem FOWTs are found to produce less total power when operating at rated speed during neutral ABL conditions. However, the downstream FOWTs exhibit power gains of up to 20\%, indicating that larger floating wind farms are likely to generate more electricity than traditional bottom-fixed offshore wind farms. ...
The growing global demand for green energy requires the development of innovative solutions. Floating offshore wind turbines (FOWTs) present significant potential by enabling the harvesting of wind in deep-sea waters. However, as this technology is still in its early stages, the impact of the added six degrees of freedom (6-DOF) motion on wake dynamics and power performance remains unclear. Understanding these effects is critical for the design of floating wind farms. In this work, large eddy simulations and an actuator line model are employed to investigate the wake behavior and power output of the IEA-15MW reference turbine mounted on several floating platform concepts. To explore wake interactions in wind farm scenarios, two tandem FOWTs are modelled. The simulations are carried out in AMR-Wind, coupled with OpenFAST for the platform and blades' motion.
Initial cases are conducted with a laminar inflow to isolate fundamental wake mechanisms. Then, a neutral atmospheric boundary layer (ABL) is recreated to perform investigations with realistic offshore conditions.
Platform motions are found to induce velocity fluctuations in the wake and promote vortex-pairing. High-frequency and large-amplitude motions particularly enhance wake recovery in the upstream wake, although this effect is less pronounced in the downstream wake. Among all 6-DOF, surge is identified as the main driver of wake dynamics.
FOWTs generate larger turbulence levels than their fixed counterparts during laminar inflow. Notably, when the downstream turbine moves in phase with the incoming wake, the resulting interaction leads to the superimposition of the wake structures, forming large, separated low-speed regions.
In contrast, under turbulent conditions, FOWTs generate lower turbulence in the near wake, but similar levels further downstream. In these cases, the differences in mean wake velocity between floating and fixed turbines decrease significantly with distance.
The tandem FOWTs are found to produce less total power when operating at rated speed during neutral ABL conditions. However, the downstream FOWTs exhibit power gains of up to 20\%, indicating that larger floating wind farms are likely to generate more electricity than traditional bottom-fixed offshore wind farms.
Initial cases are conducted with a laminar inflow to isolate fundamental wake mechanisms. Then, a neutral atmospheric boundary layer (ABL) is recreated to perform investigations with realistic offshore conditions.
Platform motions are found to induce velocity fluctuations in the wake and promote vortex-pairing. High-frequency and large-amplitude motions particularly enhance wake recovery in the upstream wake, although this effect is less pronounced in the downstream wake. Among all 6-DOF, surge is identified as the main driver of wake dynamics.
FOWTs generate larger turbulence levels than their fixed counterparts during laminar inflow. Notably, when the downstream turbine moves in phase with the incoming wake, the resulting interaction leads to the superimposition of the wake structures, forming large, separated low-speed regions.
In contrast, under turbulent conditions, FOWTs generate lower turbulence in the near wake, but similar levels further downstream. In these cases, the differences in mean wake velocity between floating and fixed turbines decrease significantly with distance.
The tandem FOWTs are found to produce less total power when operating at rated speed during neutral ABL conditions. However, the downstream FOWTs exhibit power gains of up to 20\%, indicating that larger floating wind farms are likely to generate more electricity than traditional bottom-fixed offshore wind farms.
Despite a large number of numerical and experimental tests and, as of late, linear stability analyses, incomplete knowledge of the physics of manipulated wakes prevents the community from embedding dynamic induction control into analytical control-oriented models. This thesis addresses this gap. We first focused on the framework: we considered the need of modeling blade flexibility in the simulations, then evaluated the turbine model to be used [2]. The actuator line model has been the go-to approach for wind turbines and (small) wind farm simulations in academic contexts for more than a decade but no consensus has been reached over important issues, such as evaluating the free-stream velocity and choosing the width of the smearing function used to project volume forces into the computational domain. The thesis discusses how these issues are connected and proposes an alternative approach to velocity sampling [3].
However confident we can be in our high-fidelity computational framework, it is clear that it cannot be directly used for the optimization of wind farm control strategies as this should, ideally, happen in real-time. However, the results of high-fidelity simulations can be used for reduced order modeling. We simulated turbines in both idealized [1] and realistic [4] atmospheric conditions and with both standard control and dynamic induction control. The data was organized into snapshot matrices and fed to a dynamic mode decomposition (DMD) algorithm. DMD splits the data into purely spatial modes, scalar amplitudes, and purely temporal signals. This makes it suitable for the identification of dominant frequencies. With this approach, a reduced order model is obtained, which, for the analysed cases, is able to reconstruct the full flow field with a maximum 9% relative root mean square error, with only two modes. ...
However confident we can be in our high-fidelity computational framework, it is clear that it cannot be directly used for the optimization of wind farm control strategies as this should, ideally, happen in real-time. However, the results of high-fidelity simulations can be used for reduced order modeling. We simulated turbines in both idealized [1] and realistic [4] atmospheric conditions and with both standard control and dynamic induction control. The data was organized into snapshot matrices and fed to a dynamic mode decomposition (DMD) algorithm. DMD splits the data into purely spatial modes, scalar amplitudes, and purely temporal signals. This makes it suitable for the identification of dominant frequencies. With this approach, a reduced order model is obtained, which, for the analysed cases, is able to reconstruct the full flow field with a maximum 9% relative root mean square error, with only two modes. ...
Despite a large number of numerical and experimental tests and, as of late, linear stability analyses, incomplete knowledge of the physics of manipulated wakes prevents the community from embedding dynamic induction control into analytical control-oriented models. This thesis addresses this gap. We first focused on the framework: we considered the need of modeling blade flexibility in the simulations, then evaluated the turbine model to be used [2]. The actuator line model has been the go-to approach for wind turbines and (small) wind farm simulations in academic contexts for more than a decade but no consensus has been reached over important issues, such as evaluating the free-stream velocity and choosing the width of the smearing function used to project volume forces into the computational domain. The thesis discusses how these issues are connected and proposes an alternative approach to velocity sampling [3].
However confident we can be in our high-fidelity computational framework, it is clear that it cannot be directly used for the optimization of wind farm control strategies as this should, ideally, happen in real-time. However, the results of high-fidelity simulations can be used for reduced order modeling. We simulated turbines in both idealized [1] and realistic [4] atmospheric conditions and with both standard control and dynamic induction control. The data was organized into snapshot matrices and fed to a dynamic mode decomposition (DMD) algorithm. DMD splits the data into purely spatial modes, scalar amplitudes, and purely temporal signals. This makes it suitable for the identification of dominant frequencies. With this approach, a reduced order model is obtained, which, for the analysed cases, is able to reconstruct the full flow field with a maximum 9% relative root mean square error, with only two modes.
However confident we can be in our high-fidelity computational framework, it is clear that it cannot be directly used for the optimization of wind farm control strategies as this should, ideally, happen in real-time. However, the results of high-fidelity simulations can be used for reduced order modeling. We simulated turbines in both idealized [1] and realistic [4] atmospheric conditions and with both standard control and dynamic induction control. The data was organized into snapshot matrices and fed to a dynamic mode decomposition (DMD) algorithm. DMD splits the data into purely spatial modes, scalar amplitudes, and purely temporal signals. This makes it suitable for the identification of dominant frequencies. With this approach, a reduced order model is obtained, which, for the analysed cases, is able to reconstruct the full flow field with a maximum 9% relative root mean square error, with only two modes.
In this thesis, the effect of a surging and pitching motion of a floating wind turbine on its wake as well as the effect of a surging, pitching and yawing motion on a stationary downstream turbine are analysed. Two experiments are conducted in order to address both points. The turbine models used are scale models of the DTU 10 MW turbine, and the upstream turbine is placed onto a kinematic robot which can move in all 6 degrees of freedom. The first objective is studied using Particle Tracking Velocimetry and the second objective using load cell measurements. It is found that pitch has a larger effect on the flow field in the wake than surge and that the effects of low frequency motion are more visible than those of high frequency motion. Moreover, it is found that the tracing particles are concentrated in specific areas, affecting the accuracy of the results. In future research, it should be examined why this is the case and how it can be improved. Additionally, low frequency pitch and surge had the greatest effect on the loads. A clear sinusoidal motion is visible and the mean thrust, torque, and power is increased. The effect of the high frequency motions is not visible in the results. In future studies, the wake behind a downstream turbine can also be examined using Particle Tracking Velocimetry. Moreover, the downstream turbine can also be moving rather than standing still and more movements of the turbine can be examined.
...
In this thesis, the effect of a surging and pitching motion of a floating wind turbine on its wake as well as the effect of a surging, pitching and yawing motion on a stationary downstream turbine are analysed. Two experiments are conducted in order to address both points. The turbine models used are scale models of the DTU 10 MW turbine, and the upstream turbine is placed onto a kinematic robot which can move in all 6 degrees of freedom. The first objective is studied using Particle Tracking Velocimetry and the second objective using load cell measurements. It is found that pitch has a larger effect on the flow field in the wake than surge and that the effects of low frequency motion are more visible than those of high frequency motion. Moreover, it is found that the tracing particles are concentrated in specific areas, affecting the accuracy of the results. In future research, it should be examined why this is the case and how it can be improved. Additionally, low frequency pitch and surge had the greatest effect on the loads. A clear sinusoidal motion is visible and the mean thrust, torque, and power is increased. The effect of the high frequency motions is not visible in the results. In future studies, the wake behind a downstream turbine can also be examined using Particle Tracking Velocimetry. Moreover, the downstream turbine can also be moving rather than standing still and more movements of the turbine can be examined.
Experimental testing of floating offshore wind turbines (FOWT) is essential for understanding the various engineering challenges posed by their dynamic motion. For example, the pitch and surge motion of the FOWT results in unsteady aerodynamic effects caused by wake interaction. In this project, two FOWT models (DTU10MW with TripleSpar and IEA15MW with VolturnUS) were developed for testing with a hybrid hardware-in-loop (HIL) setup. This setup integrates physical wind loading in a wind tunnel with numerically simulated hydrodynamic loading.
The HIL setup comprises a scaled wind turbine model, a hexapod that can actuate the floating motion, an instrumentation system with sensors to measure forces and accelerations, and a numerical model that simulates FOWT dynamics in real-time based on measured data. This work explores the methodology of developing a numerical model that can simulate the dynamics of the scaled FOWT model in real-time with the applied wind loading and numerically simulated hydrodynamic loading. Furthermore, a methodology for correcting the measured forces to obtain external aerodynamic forces is introduced and validated.
The process of developing the numerical model is thoroughly detailed, including its tuning and validation. The challenges encountered during the validation process are discussed in depth, with an emphasis on their underlying causes. Finally, the HIL numerical model was used to test the scaled DTU10MW wind turbine with a simulated TripleSpar floater at the Open Jet Facility (OJF) wind tunnel at TU Delft. This study also presents intriguing results regarding the HIL model's performance and the impact of wind on FOWT dynamics observed during the test campaign. ...
The HIL setup comprises a scaled wind turbine model, a hexapod that can actuate the floating motion, an instrumentation system with sensors to measure forces and accelerations, and a numerical model that simulates FOWT dynamics in real-time based on measured data. This work explores the methodology of developing a numerical model that can simulate the dynamics of the scaled FOWT model in real-time with the applied wind loading and numerically simulated hydrodynamic loading. Furthermore, a methodology for correcting the measured forces to obtain external aerodynamic forces is introduced and validated.
The process of developing the numerical model is thoroughly detailed, including its tuning and validation. The challenges encountered during the validation process are discussed in depth, with an emphasis on their underlying causes. Finally, the HIL numerical model was used to test the scaled DTU10MW wind turbine with a simulated TripleSpar floater at the Open Jet Facility (OJF) wind tunnel at TU Delft. This study also presents intriguing results regarding the HIL model's performance and the impact of wind on FOWT dynamics observed during the test campaign. ...
Experimental testing of floating offshore wind turbines (FOWT) is essential for understanding the various engineering challenges posed by their dynamic motion. For example, the pitch and surge motion of the FOWT results in unsteady aerodynamic effects caused by wake interaction. In this project, two FOWT models (DTU10MW with TripleSpar and IEA15MW with VolturnUS) were developed for testing with a hybrid hardware-in-loop (HIL) setup. This setup integrates physical wind loading in a wind tunnel with numerically simulated hydrodynamic loading.
The HIL setup comprises a scaled wind turbine model, a hexapod that can actuate the floating motion, an instrumentation system with sensors to measure forces and accelerations, and a numerical model that simulates FOWT dynamics in real-time based on measured data. This work explores the methodology of developing a numerical model that can simulate the dynamics of the scaled FOWT model in real-time with the applied wind loading and numerically simulated hydrodynamic loading. Furthermore, a methodology for correcting the measured forces to obtain external aerodynamic forces is introduced and validated.
The process of developing the numerical model is thoroughly detailed, including its tuning and validation. The challenges encountered during the validation process are discussed in depth, with an emphasis on their underlying causes. Finally, the HIL numerical model was used to test the scaled DTU10MW wind turbine with a simulated TripleSpar floater at the Open Jet Facility (OJF) wind tunnel at TU Delft. This study also presents intriguing results regarding the HIL model's performance and the impact of wind on FOWT dynamics observed during the test campaign.
The HIL setup comprises a scaled wind turbine model, a hexapod that can actuate the floating motion, an instrumentation system with sensors to measure forces and accelerations, and a numerical model that simulates FOWT dynamics in real-time based on measured data. This work explores the methodology of developing a numerical model that can simulate the dynamics of the scaled FOWT model in real-time with the applied wind loading and numerically simulated hydrodynamic loading. Furthermore, a methodology for correcting the measured forces to obtain external aerodynamic forces is introduced and validated.
The process of developing the numerical model is thoroughly detailed, including its tuning and validation. The challenges encountered during the validation process are discussed in depth, with an emphasis on their underlying causes. Finally, the HIL numerical model was used to test the scaled DTU10MW wind turbine with a simulated TripleSpar floater at the Open Jet Facility (OJF) wind tunnel at TU Delft. This study also presents intriguing results regarding the HIL model's performance and the impact of wind on FOWT dynamics observed during the test campaign.
Floating offshore wind farms are a promising technological development that can help provide large amounts of energy from sites previously not feasible for wind farm development. To suc- cessfully develop floating wind farms, knowledge about the involved aerodynamics are crucial. A first building block of this is the understanding of the wake of a single floating wind turbine un- dergoing floating motion in turbulent wind fields. Using an actuator line model within the Large Eddy Simulation framework YALES2 the wake of a floating wind turbine undergoing surge motion has been investigated at varying amplitudes and frequencies. Special attention was given to the inflow conditions, comparing laminar and low turbulent inflow conditions modeled using Mann turbulence boxes. Varying the surge amplitude at a surge frequency equivalent to St = 0.4, the investigations show that the influence of the motion on the wake is greatly reduced even when low turbulence levels are present. Already at a turbulence intensity of 2.5% the wake deficit and wake turbulence in- tensity were indistinguishable from a fixed turbine’s wake for lower surge amplitudes. Large surge amplitudes were required to significantly change these wake statistics, starting from an amplitude of A/D = 0.16 rotor diameters.Using proper orthogonal decomposition the dynamics in the wake have been analyzed. It was found that the effect of the rotor motion on the wake is lower when the inflow turbulence intensity is increased. Varying the motion frequency in the range of St ∈ [0.2, 0.8] at a amplitude of A/D = 0.04, it was shown that a surge motion at a frequency corresponding to St = 0.6 has the highest impact on the wake in terms of the reduction of modal time series correlations. However, even in that case the wake deficit and turbulence intensity in the wake are not changed significantly.
...
Floating offshore wind farms are a promising technological development that can help provide large amounts of energy from sites previously not feasible for wind farm development. To suc- cessfully develop floating wind farms, knowledge about the involved aerodynamics are crucial. A first building block of this is the understanding of the wake of a single floating wind turbine un- dergoing floating motion in turbulent wind fields. Using an actuator line model within the Large Eddy Simulation framework YALES2 the wake of a floating wind turbine undergoing surge motion has been investigated at varying amplitudes and frequencies. Special attention was given to the inflow conditions, comparing laminar and low turbulent inflow conditions modeled using Mann turbulence boxes. Varying the surge amplitude at a surge frequency equivalent to St = 0.4, the investigations show that the influence of the motion on the wake is greatly reduced even when low turbulence levels are present. Already at a turbulence intensity of 2.5% the wake deficit and wake turbulence in- tensity were indistinguishable from a fixed turbine’s wake for lower surge amplitudes. Large surge amplitudes were required to significantly change these wake statistics, starting from an amplitude of A/D = 0.16 rotor diameters.Using proper orthogonal decomposition the dynamics in the wake have been analyzed. It was found that the effect of the rotor motion on the wake is lower when the inflow turbulence intensity is increased. Varying the motion frequency in the range of St ∈ [0.2, 0.8] at a amplitude of A/D = 0.04, it was shown that a surge motion at a frequency corresponding to St = 0.6 has the highest impact on the wake in terms of the reduction of modal time series correlations. However, even in that case the wake deficit and turbulence intensity in the wake are not changed significantly.
The field of floating offshore wind energy is rapidly advancing, offering a promising solution to
harness stronger and more consistent wind resources in deep-water regions, where conventional bottom-fixed turbines are not viable. Floating Offshore Wind Turbines (FOWTs), mounted on floating substructures, enable the deployment of wind turbines in deep offshore locations without the need for extensive foundations. This approach can significantly lower project costs, primarily by reducing substructure procurement expenses for comparable water depths. As a result, the installed capacity of FOWTs is projected to reach approximately 18.9 GW by 2030.
However, the design of cost-efficient FOWTs presents significant challenges due to the complex interactions between aerodynamic and hydrodynamic loads, as well as the coupling of various subsystems. One of the key hurdles is reducing the Levelized Cost of Energy (LCoE) to ensure the economic viability of Floating Offshore Wind Farms (FOWFs). Additionally, the flexibility of the floating substructure, which moves in six degrees of freedom (DOF), introduces complexities in the wake profile, such as enhanced wake meandering, which may affect the wake losses and the overall farm energy production.
Despite the potential impact of floater movements on energy yield, the specific effects on optimal wind farm layout design remain under-explored. Furthermore, the impact of these displacements on the LCoE needs to be examined to determine whether incorporating floater displacements into optimization frameworks leads to more accurate results or merely adds unnecessary complexity and computational cost.
This thesis evaluates the role of static floater displacements, specifically tilt and surge, in the optimization of FOWFs. Two distinct scenarios have been compared to assess how these displacements affect energy yield, the LCoE, and the optimal wind farm layout. The first scenario (Case A) disregards the static equilibrium displacements of the floating turbines, while the second scenario (Case B) incorporates these displacements into the analysis. To conduct the analysis, this thesis develops a wake modeling strategy focusing on wind-induced floater displacements, wherein the tilt and surge displacements of the floater are computed for each turbine using the PyWake framework, based on the effective wind speed experienced by its rotor. This wake modeling approach is then integrated into an OpenMDAO-based optimization framework to minimize the LCoE of the wind farm, which is used for the comparison between the two cases, to achieve the objectives of this study.
A case study of a 42-turbine sample wind farm in the North Sea, arranged in a rectangular gridded layout, was analyzed using the optimization framework. The results showed minimal differences in AEP, LCoE, and layout design between the scenarios that account for floater displacements (Case B) and those that neglect them (Case A). However, the computational time required for the optimization process increased by approximately fourfold for Case B. Case B resulted in a 0.25% reduction in AEP and a 0.162 €/MWh increase in LCoE, primarily due to changes in wake behavior caused by tilt and surge displacements. The optimal layout also shifted in orientation and spacing, with Case B yielding slightly lower power in the final configuration. Despite these effects, the overall impact on the LCoE was modest, suggesting that while incorporating floater displacements could potentially improve the accuracy of results for FOWFs, it might not justify the added computational costs for larger wind farms. ...
harness stronger and more consistent wind resources in deep-water regions, where conventional bottom-fixed turbines are not viable. Floating Offshore Wind Turbines (FOWTs), mounted on floating substructures, enable the deployment of wind turbines in deep offshore locations without the need for extensive foundations. This approach can significantly lower project costs, primarily by reducing substructure procurement expenses for comparable water depths. As a result, the installed capacity of FOWTs is projected to reach approximately 18.9 GW by 2030.
However, the design of cost-efficient FOWTs presents significant challenges due to the complex interactions between aerodynamic and hydrodynamic loads, as well as the coupling of various subsystems. One of the key hurdles is reducing the Levelized Cost of Energy (LCoE) to ensure the economic viability of Floating Offshore Wind Farms (FOWFs). Additionally, the flexibility of the floating substructure, which moves in six degrees of freedom (DOF), introduces complexities in the wake profile, such as enhanced wake meandering, which may affect the wake losses and the overall farm energy production.
Despite the potential impact of floater movements on energy yield, the specific effects on optimal wind farm layout design remain under-explored. Furthermore, the impact of these displacements on the LCoE needs to be examined to determine whether incorporating floater displacements into optimization frameworks leads to more accurate results or merely adds unnecessary complexity and computational cost.
This thesis evaluates the role of static floater displacements, specifically tilt and surge, in the optimization of FOWFs. Two distinct scenarios have been compared to assess how these displacements affect energy yield, the LCoE, and the optimal wind farm layout. The first scenario (Case A) disregards the static equilibrium displacements of the floating turbines, while the second scenario (Case B) incorporates these displacements into the analysis. To conduct the analysis, this thesis develops a wake modeling strategy focusing on wind-induced floater displacements, wherein the tilt and surge displacements of the floater are computed for each turbine using the PyWake framework, based on the effective wind speed experienced by its rotor. This wake modeling approach is then integrated into an OpenMDAO-based optimization framework to minimize the LCoE of the wind farm, which is used for the comparison between the two cases, to achieve the objectives of this study.
A case study of a 42-turbine sample wind farm in the North Sea, arranged in a rectangular gridded layout, was analyzed using the optimization framework. The results showed minimal differences in AEP, LCoE, and layout design between the scenarios that account for floater displacements (Case B) and those that neglect them (Case A). However, the computational time required for the optimization process increased by approximately fourfold for Case B. Case B resulted in a 0.25% reduction in AEP and a 0.162 €/MWh increase in LCoE, primarily due to changes in wake behavior caused by tilt and surge displacements. The optimal layout also shifted in orientation and spacing, with Case B yielding slightly lower power in the final configuration. Despite these effects, the overall impact on the LCoE was modest, suggesting that while incorporating floater displacements could potentially improve the accuracy of results for FOWFs, it might not justify the added computational costs for larger wind farms. ...
The field of floating offshore wind energy is rapidly advancing, offering a promising solution to
harness stronger and more consistent wind resources in deep-water regions, where conventional bottom-fixed turbines are not viable. Floating Offshore Wind Turbines (FOWTs), mounted on floating substructures, enable the deployment of wind turbines in deep offshore locations without the need for extensive foundations. This approach can significantly lower project costs, primarily by reducing substructure procurement expenses for comparable water depths. As a result, the installed capacity of FOWTs is projected to reach approximately 18.9 GW by 2030.
However, the design of cost-efficient FOWTs presents significant challenges due to the complex interactions between aerodynamic and hydrodynamic loads, as well as the coupling of various subsystems. One of the key hurdles is reducing the Levelized Cost of Energy (LCoE) to ensure the economic viability of Floating Offshore Wind Farms (FOWFs). Additionally, the flexibility of the floating substructure, which moves in six degrees of freedom (DOF), introduces complexities in the wake profile, such as enhanced wake meandering, which may affect the wake losses and the overall farm energy production.
Despite the potential impact of floater movements on energy yield, the specific effects on optimal wind farm layout design remain under-explored. Furthermore, the impact of these displacements on the LCoE needs to be examined to determine whether incorporating floater displacements into optimization frameworks leads to more accurate results or merely adds unnecessary complexity and computational cost.
This thesis evaluates the role of static floater displacements, specifically tilt and surge, in the optimization of FOWFs. Two distinct scenarios have been compared to assess how these displacements affect energy yield, the LCoE, and the optimal wind farm layout. The first scenario (Case A) disregards the static equilibrium displacements of the floating turbines, while the second scenario (Case B) incorporates these displacements into the analysis. To conduct the analysis, this thesis develops a wake modeling strategy focusing on wind-induced floater displacements, wherein the tilt and surge displacements of the floater are computed for each turbine using the PyWake framework, based on the effective wind speed experienced by its rotor. This wake modeling approach is then integrated into an OpenMDAO-based optimization framework to minimize the LCoE of the wind farm, which is used for the comparison between the two cases, to achieve the objectives of this study.
A case study of a 42-turbine sample wind farm in the North Sea, arranged in a rectangular gridded layout, was analyzed using the optimization framework. The results showed minimal differences in AEP, LCoE, and layout design between the scenarios that account for floater displacements (Case B) and those that neglect them (Case A). However, the computational time required for the optimization process increased by approximately fourfold for Case B. Case B resulted in a 0.25% reduction in AEP and a 0.162 €/MWh increase in LCoE, primarily due to changes in wake behavior caused by tilt and surge displacements. The optimal layout also shifted in orientation and spacing, with Case B yielding slightly lower power in the final configuration. Despite these effects, the overall impact on the LCoE was modest, suggesting that while incorporating floater displacements could potentially improve the accuracy of results for FOWFs, it might not justify the added computational costs for larger wind farms.
harness stronger and more consistent wind resources in deep-water regions, where conventional bottom-fixed turbines are not viable. Floating Offshore Wind Turbines (FOWTs), mounted on floating substructures, enable the deployment of wind turbines in deep offshore locations without the need for extensive foundations. This approach can significantly lower project costs, primarily by reducing substructure procurement expenses for comparable water depths. As a result, the installed capacity of FOWTs is projected to reach approximately 18.9 GW by 2030.
However, the design of cost-efficient FOWTs presents significant challenges due to the complex interactions between aerodynamic and hydrodynamic loads, as well as the coupling of various subsystems. One of the key hurdles is reducing the Levelized Cost of Energy (LCoE) to ensure the economic viability of Floating Offshore Wind Farms (FOWFs). Additionally, the flexibility of the floating substructure, which moves in six degrees of freedom (DOF), introduces complexities in the wake profile, such as enhanced wake meandering, which may affect the wake losses and the overall farm energy production.
Despite the potential impact of floater movements on energy yield, the specific effects on optimal wind farm layout design remain under-explored. Furthermore, the impact of these displacements on the LCoE needs to be examined to determine whether incorporating floater displacements into optimization frameworks leads to more accurate results or merely adds unnecessary complexity and computational cost.
This thesis evaluates the role of static floater displacements, specifically tilt and surge, in the optimization of FOWFs. Two distinct scenarios have been compared to assess how these displacements affect energy yield, the LCoE, and the optimal wind farm layout. The first scenario (Case A) disregards the static equilibrium displacements of the floating turbines, while the second scenario (Case B) incorporates these displacements into the analysis. To conduct the analysis, this thesis develops a wake modeling strategy focusing on wind-induced floater displacements, wherein the tilt and surge displacements of the floater are computed for each turbine using the PyWake framework, based on the effective wind speed experienced by its rotor. This wake modeling approach is then integrated into an OpenMDAO-based optimization framework to minimize the LCoE of the wind farm, which is used for the comparison between the two cases, to achieve the objectives of this study.
A case study of a 42-turbine sample wind farm in the North Sea, arranged in a rectangular gridded layout, was analyzed using the optimization framework. The results showed minimal differences in AEP, LCoE, and layout design between the scenarios that account for floater displacements (Case B) and those that neglect them (Case A). However, the computational time required for the optimization process increased by approximately fourfold for Case B. Case B resulted in a 0.25% reduction in AEP and a 0.162 €/MWh increase in LCoE, primarily due to changes in wake behavior caused by tilt and surge displacements. The optimal layout also shifted in orientation and spacing, with Case B yielding slightly lower power in the final configuration. Despite these effects, the overall impact on the LCoE was modest, suggesting that while incorporating floater displacements could potentially improve the accuracy of results for FOWFs, it might not justify the added computational costs for larger wind farms.
Accurate wind turbine modelling is essential for reliable aerodynamic performance predictions. The industry primarily uses the Blade Element Momentum (BEM) method with correction models, but BEM’s assumptions become less valid with larger rotors and in Floating Offshore Wind Turbines (FOWTs), where wave interactions and wake dynamics are more complex. The Lifting Line Free Vortex Wake (LLFVW) method offers higher modelling fidelity but is less computationally efficient.
This study compares a BEM and LLFVW model implemented in the software QBlade. The evaluated parameters include power, torque, thrust, root bending moment, tip deflection, and angle of attack using the floating 15 MW UMaine VolturnUS-S reference turbine under various wind and wave conditions taken from several Design Load Cases (DLCs). The aim is to identify any differences between the methods and the met-ocean conditions under which these are most pronounced.
The results show minimal differences in BEM and LLFVW outputs under varying wave conditions. However, wind conditions have a greater impact, particularly around rated speeds where discrepancies were observed, mainly due to different controller dynamics. Above-rated conditions showed similar power, torque, and thrust predictions, but notable differences in angle of attack. The maximum and standard deviation of the root bending moment and tip deflection were found to be consistently lower for LLFVW compared to BEM. ...
This study compares a BEM and LLFVW model implemented in the software QBlade. The evaluated parameters include power, torque, thrust, root bending moment, tip deflection, and angle of attack using the floating 15 MW UMaine VolturnUS-S reference turbine under various wind and wave conditions taken from several Design Load Cases (DLCs). The aim is to identify any differences between the methods and the met-ocean conditions under which these are most pronounced.
The results show minimal differences in BEM and LLFVW outputs under varying wave conditions. However, wind conditions have a greater impact, particularly around rated speeds where discrepancies were observed, mainly due to different controller dynamics. Above-rated conditions showed similar power, torque, and thrust predictions, but notable differences in angle of attack. The maximum and standard deviation of the root bending moment and tip deflection were found to be consistently lower for LLFVW compared to BEM. ...
Accurate wind turbine modelling is essential for reliable aerodynamic performance predictions. The industry primarily uses the Blade Element Momentum (BEM) method with correction models, but BEM’s assumptions become less valid with larger rotors and in Floating Offshore Wind Turbines (FOWTs), where wave interactions and wake dynamics are more complex. The Lifting Line Free Vortex Wake (LLFVW) method offers higher modelling fidelity but is less computationally efficient.
This study compares a BEM and LLFVW model implemented in the software QBlade. The evaluated parameters include power, torque, thrust, root bending moment, tip deflection, and angle of attack using the floating 15 MW UMaine VolturnUS-S reference turbine under various wind and wave conditions taken from several Design Load Cases (DLCs). The aim is to identify any differences between the methods and the met-ocean conditions under which these are most pronounced.
The results show minimal differences in BEM and LLFVW outputs under varying wave conditions. However, wind conditions have a greater impact, particularly around rated speeds where discrepancies were observed, mainly due to different controller dynamics. Above-rated conditions showed similar power, torque, and thrust predictions, but notable differences in angle of attack. The maximum and standard deviation of the root bending moment and tip deflection were found to be consistently lower for LLFVW compared to BEM.
This study compares a BEM and LLFVW model implemented in the software QBlade. The evaluated parameters include power, torque, thrust, root bending moment, tip deflection, and angle of attack using the floating 15 MW UMaine VolturnUS-S reference turbine under various wind and wave conditions taken from several Design Load Cases (DLCs). The aim is to identify any differences between the methods and the met-ocean conditions under which these are most pronounced.
The results show minimal differences in BEM and LLFVW outputs under varying wave conditions. However, wind conditions have a greater impact, particularly around rated speeds where discrepancies were observed, mainly due to different controller dynamics. Above-rated conditions showed similar power, torque, and thrust predictions, but notable differences in angle of attack. The maximum and standard deviation of the root bending moment and tip deflection were found to be consistently lower for LLFVW compared to BEM.
CFD Simulation of a Large Floating Offshore Wind Turbine (FOWT)
Hydrodynamic and Aerodynamic Analysis of the IEA 15MW Wind Turbine Mounted on the VolturnUS-S Floating Platform using OpenFOAM
This thesis presents a comprehensive study on Floating Offshore Wind Turbine’s (FOWT’s) modeling and simulation, focusing on the IEA 15MW wind turbine coupled to the VolturnUS-S semi-submersible platform. The primary goal was to develop the building blocks of a high-fidelity Computational Fluid Dynamics (CFD) coupled model using OpenFOAM to simulate large-scale FOWT behavior, verify, and compare each component individually against the mid-fidelity tool OpenFAST. The project builds on the frameworks of previous studies by Pere Frontera Pericàs [81] and Scarlatti [98], expanding them to accommodate the complexities of a larger turbine and more intricate environmental conditions.
The model was implemented using OpenFOAM’s waves2Foam and Moody libraries for wave and mooring modeling, respectively, while aerodynamic simulations employed the turbinesFoam package with an actuator line approach. A spatial convergence study optimized mesh parameters for computational efficiency and accuracy, ensuring reliable and stable simulation results for motions up to 24 meters in surge. Comparisons between OpenFOAM and OpenFAST using a P-Q analysis highlighted significant differences, particularly in modeling damping. OpenFAST was shown to underestimate both linear and quadratic hydrodynamic damping due to its simplified representation of fluid dynamics. Discrepancies in the equilibrium positions were found between the decay tests using CFD; their origin still needs more investigation.
In aerodynamic simulations, steady-state and prescribed motion tests revealed critical differences in thrust and power predictions between OpenFOAM and OpenFAST. OpenFAST overpredicted the reduction in axial wind speed, leading to a reduced power output for steady turbine simulations. The CFD model provided a more accurate representation of rotor wake effects and dynamic responses, particularly in high-frequency surge conditions, where OpenFAST overestimated power fluctuations. These findings underscore the importance of using high-fidelity models like OpenFOAM to improve the accuracy of performance predictions in floating wind turbines.
The study concludes by offering recommendations for future research, including model validation using experimental data and the implementation of overset mesh techniques to improve simulation stability for large motions. The work establishes a reliable CFD framework for simulating this large FOWT, offering insights into enhancing mid-fidelity models and guiding future developments in floating wind turbine design and analysis. It also builds a fully coupled CFD model in OpenFOAM to investigate its behavior. ...
The model was implemented using OpenFOAM’s waves2Foam and Moody libraries for wave and mooring modeling, respectively, while aerodynamic simulations employed the turbinesFoam package with an actuator line approach. A spatial convergence study optimized mesh parameters for computational efficiency and accuracy, ensuring reliable and stable simulation results for motions up to 24 meters in surge. Comparisons between OpenFOAM and OpenFAST using a P-Q analysis highlighted significant differences, particularly in modeling damping. OpenFAST was shown to underestimate both linear and quadratic hydrodynamic damping due to its simplified representation of fluid dynamics. Discrepancies in the equilibrium positions were found between the decay tests using CFD; their origin still needs more investigation.
In aerodynamic simulations, steady-state and prescribed motion tests revealed critical differences in thrust and power predictions between OpenFOAM and OpenFAST. OpenFAST overpredicted the reduction in axial wind speed, leading to a reduced power output for steady turbine simulations. The CFD model provided a more accurate representation of rotor wake effects and dynamic responses, particularly in high-frequency surge conditions, where OpenFAST overestimated power fluctuations. These findings underscore the importance of using high-fidelity models like OpenFOAM to improve the accuracy of performance predictions in floating wind turbines.
The study concludes by offering recommendations for future research, including model validation using experimental data and the implementation of overset mesh techniques to improve simulation stability for large motions. The work establishes a reliable CFD framework for simulating this large FOWT, offering insights into enhancing mid-fidelity models and guiding future developments in floating wind turbine design and analysis. It also builds a fully coupled CFD model in OpenFOAM to investigate its behavior. ...
This thesis presents a comprehensive study on Floating Offshore Wind Turbine’s (FOWT’s) modeling and simulation, focusing on the IEA 15MW wind turbine coupled to the VolturnUS-S semi-submersible platform. The primary goal was to develop the building blocks of a high-fidelity Computational Fluid Dynamics (CFD) coupled model using OpenFOAM to simulate large-scale FOWT behavior, verify, and compare each component individually against the mid-fidelity tool OpenFAST. The project builds on the frameworks of previous studies by Pere Frontera Pericàs [81] and Scarlatti [98], expanding them to accommodate the complexities of a larger turbine and more intricate environmental conditions.
The model was implemented using OpenFOAM’s waves2Foam and Moody libraries for wave and mooring modeling, respectively, while aerodynamic simulations employed the turbinesFoam package with an actuator line approach. A spatial convergence study optimized mesh parameters for computational efficiency and accuracy, ensuring reliable and stable simulation results for motions up to 24 meters in surge. Comparisons between OpenFOAM and OpenFAST using a P-Q analysis highlighted significant differences, particularly in modeling damping. OpenFAST was shown to underestimate both linear and quadratic hydrodynamic damping due to its simplified representation of fluid dynamics. Discrepancies in the equilibrium positions were found between the decay tests using CFD; their origin still needs more investigation.
In aerodynamic simulations, steady-state and prescribed motion tests revealed critical differences in thrust and power predictions between OpenFOAM and OpenFAST. OpenFAST overpredicted the reduction in axial wind speed, leading to a reduced power output for steady turbine simulations. The CFD model provided a more accurate representation of rotor wake effects and dynamic responses, particularly in high-frequency surge conditions, where OpenFAST overestimated power fluctuations. These findings underscore the importance of using high-fidelity models like OpenFOAM to improve the accuracy of performance predictions in floating wind turbines.
The study concludes by offering recommendations for future research, including model validation using experimental data and the implementation of overset mesh techniques to improve simulation stability for large motions. The work establishes a reliable CFD framework for simulating this large FOWT, offering insights into enhancing mid-fidelity models and guiding future developments in floating wind turbine design and analysis. It also builds a fully coupled CFD model in OpenFOAM to investigate its behavior.
The model was implemented using OpenFOAM’s waves2Foam and Moody libraries for wave and mooring modeling, respectively, while aerodynamic simulations employed the turbinesFoam package with an actuator line approach. A spatial convergence study optimized mesh parameters for computational efficiency and accuracy, ensuring reliable and stable simulation results for motions up to 24 meters in surge. Comparisons between OpenFOAM and OpenFAST using a P-Q analysis highlighted significant differences, particularly in modeling damping. OpenFAST was shown to underestimate both linear and quadratic hydrodynamic damping due to its simplified representation of fluid dynamics. Discrepancies in the equilibrium positions were found between the decay tests using CFD; their origin still needs more investigation.
In aerodynamic simulations, steady-state and prescribed motion tests revealed critical differences in thrust and power predictions between OpenFOAM and OpenFAST. OpenFAST overpredicted the reduction in axial wind speed, leading to a reduced power output for steady turbine simulations. The CFD model provided a more accurate representation of rotor wake effects and dynamic responses, particularly in high-frequency surge conditions, where OpenFAST overestimated power fluctuations. These findings underscore the importance of using high-fidelity models like OpenFOAM to improve the accuracy of performance predictions in floating wind turbines.
The study concludes by offering recommendations for future research, including model validation using experimental data and the implementation of overset mesh techniques to improve simulation stability for large motions. The work establishes a reliable CFD framework for simulating this large FOWT, offering insights into enhancing mid-fidelity models and guiding future developments in floating wind turbine design and analysis. It also builds a fully coupled CFD model in OpenFOAM to investigate its behavior.
In recent years, the use of renewable energy sources has increased significantly. As a result, offshore wind energy has expanded due to its advantages compared to onshore, such as steadier winds, reduced visual impact and lower noise emissions. However, 80% of the global offshore wind resource potential is located in areas with water depths greater than 60 meters, making the installation of fixed-bottom wind turbines unfeasible and leading to an increase in floating wind turbines. Floating offshore wind turbines, due to their increased freedom of movement, experience more complex aerodynamic and hydrodynamic phenomena, resulting in different power generation compared to fixed-bottom wind turbines. The effect of six degrees of freedom on power production has not been extensively studied in full-scale operating wind turbines. Additionally, a better prediction of energy production could also reduce the investment risk of floating wind turbines, leading to a reduced levelised cost of energy and better integration of floating wind turbines into a fully renewable energy system.
The aim of this report was to investigate the power generation and response of the TetraSpar demonstrator project, the world’s first fully industrialised floating offshore foundation. To achieve this, a model of the TetraSpar demonstrator was created in OpenFAST. Due to confidentiality, some data, particularly regarding the wind turbine, were unavailable and were instead based on scaling the NREL 5 MW wind turbine. In addition, the ROSCO controller was incorporated into the model and the floating platform was modelled as a six degrees of freedom rigid body.
For the validation of the OpenFAST model, a comparison was performed with the on-site TetraSpar demonstrator. Data for the on-site demonstrator regarding motions, metocean conditions and power generation were provided by RWE. Further filtering and averaging the data in 10-minute periods for an entire year was performed. First, three one-hour simulations were performed for three different operating conditions: cut-in, below rated and above rated. Results from time series and power spectral density analyses showed a good agreement in platform motions. For the generated power, the mean average produced power was closest to the on-site data in the above rated region.
Next, for the power curve and AEP estimation, the probability of occurrence for each wind speed in the operating region was determined based on the on-site measured data. Furthermore, for every wind speed, the most representative significant wave height, wave period, turbulence intensity and other parameters needed as inputs in OpenFAST were found. The results showed that the AEP predicted by OpenFAST was 2.8% lower than the AEP measured at the on-site TetraSpar demonstrator. Moreover, different peak shaving levels of the controller contributed to the AEP percentage difference, ranging from about 4.4% to 2.1% compared to the on-site measurements. As far as platform motions are concerned, the model showed good agreement, capturing the trend of mean values and standard deviations of the two most dominant motions in power production, surge and pitch. In addition, the effect of wave height and wave period showed that the mean generated power is slightly affected. Similar results were also found for different current velocities. However, waves were found to influence the oscillation amplitudes of surge and pitch motions, while currents mainly affected the shift in mean values. Lastly, wind-wave misalignment also proved to have a negative effect on power performance.
In conclusion, the current OpenFAST model can be used to estimate the energy production and platform motion of the TetraSpar demonstrator. However, further improvements and reduction of the assumptions made, mainly due to the unavailability of data because of confidentiality, could enhance its accuracy. ...
The aim of this report was to investigate the power generation and response of the TetraSpar demonstrator project, the world’s first fully industrialised floating offshore foundation. To achieve this, a model of the TetraSpar demonstrator was created in OpenFAST. Due to confidentiality, some data, particularly regarding the wind turbine, were unavailable and were instead based on scaling the NREL 5 MW wind turbine. In addition, the ROSCO controller was incorporated into the model and the floating platform was modelled as a six degrees of freedom rigid body.
For the validation of the OpenFAST model, a comparison was performed with the on-site TetraSpar demonstrator. Data for the on-site demonstrator regarding motions, metocean conditions and power generation were provided by RWE. Further filtering and averaging the data in 10-minute periods for an entire year was performed. First, three one-hour simulations were performed for three different operating conditions: cut-in, below rated and above rated. Results from time series and power spectral density analyses showed a good agreement in platform motions. For the generated power, the mean average produced power was closest to the on-site data in the above rated region.
Next, for the power curve and AEP estimation, the probability of occurrence for each wind speed in the operating region was determined based on the on-site measured data. Furthermore, for every wind speed, the most representative significant wave height, wave period, turbulence intensity and other parameters needed as inputs in OpenFAST were found. The results showed that the AEP predicted by OpenFAST was 2.8% lower than the AEP measured at the on-site TetraSpar demonstrator. Moreover, different peak shaving levels of the controller contributed to the AEP percentage difference, ranging from about 4.4% to 2.1% compared to the on-site measurements. As far as platform motions are concerned, the model showed good agreement, capturing the trend of mean values and standard deviations of the two most dominant motions in power production, surge and pitch. In addition, the effect of wave height and wave period showed that the mean generated power is slightly affected. Similar results were also found for different current velocities. However, waves were found to influence the oscillation amplitudes of surge and pitch motions, while currents mainly affected the shift in mean values. Lastly, wind-wave misalignment also proved to have a negative effect on power performance.
In conclusion, the current OpenFAST model can be used to estimate the energy production and platform motion of the TetraSpar demonstrator. However, further improvements and reduction of the assumptions made, mainly due to the unavailability of data because of confidentiality, could enhance its accuracy. ...
In recent years, the use of renewable energy sources has increased significantly. As a result, offshore wind energy has expanded due to its advantages compared to onshore, such as steadier winds, reduced visual impact and lower noise emissions. However, 80% of the global offshore wind resource potential is located in areas with water depths greater than 60 meters, making the installation of fixed-bottom wind turbines unfeasible and leading to an increase in floating wind turbines. Floating offshore wind turbines, due to their increased freedom of movement, experience more complex aerodynamic and hydrodynamic phenomena, resulting in different power generation compared to fixed-bottom wind turbines. The effect of six degrees of freedom on power production has not been extensively studied in full-scale operating wind turbines. Additionally, a better prediction of energy production could also reduce the investment risk of floating wind turbines, leading to a reduced levelised cost of energy and better integration of floating wind turbines into a fully renewable energy system.
The aim of this report was to investigate the power generation and response of the TetraSpar demonstrator project, the world’s first fully industrialised floating offshore foundation. To achieve this, a model of the TetraSpar demonstrator was created in OpenFAST. Due to confidentiality, some data, particularly regarding the wind turbine, were unavailable and were instead based on scaling the NREL 5 MW wind turbine. In addition, the ROSCO controller was incorporated into the model and the floating platform was modelled as a six degrees of freedom rigid body.
For the validation of the OpenFAST model, a comparison was performed with the on-site TetraSpar demonstrator. Data for the on-site demonstrator regarding motions, metocean conditions and power generation were provided by RWE. Further filtering and averaging the data in 10-minute periods for an entire year was performed. First, three one-hour simulations were performed for three different operating conditions: cut-in, below rated and above rated. Results from time series and power spectral density analyses showed a good agreement in platform motions. For the generated power, the mean average produced power was closest to the on-site data in the above rated region.
Next, for the power curve and AEP estimation, the probability of occurrence for each wind speed in the operating region was determined based on the on-site measured data. Furthermore, for every wind speed, the most representative significant wave height, wave period, turbulence intensity and other parameters needed as inputs in OpenFAST were found. The results showed that the AEP predicted by OpenFAST was 2.8% lower than the AEP measured at the on-site TetraSpar demonstrator. Moreover, different peak shaving levels of the controller contributed to the AEP percentage difference, ranging from about 4.4% to 2.1% compared to the on-site measurements. As far as platform motions are concerned, the model showed good agreement, capturing the trend of mean values and standard deviations of the two most dominant motions in power production, surge and pitch. In addition, the effect of wave height and wave period showed that the mean generated power is slightly affected. Similar results were also found for different current velocities. However, waves were found to influence the oscillation amplitudes of surge and pitch motions, while currents mainly affected the shift in mean values. Lastly, wind-wave misalignment also proved to have a negative effect on power performance.
In conclusion, the current OpenFAST model can be used to estimate the energy production and platform motion of the TetraSpar demonstrator. However, further improvements and reduction of the assumptions made, mainly due to the unavailability of data because of confidentiality, could enhance its accuracy.
The aim of this report was to investigate the power generation and response of the TetraSpar demonstrator project, the world’s first fully industrialised floating offshore foundation. To achieve this, a model of the TetraSpar demonstrator was created in OpenFAST. Due to confidentiality, some data, particularly regarding the wind turbine, were unavailable and were instead based on scaling the NREL 5 MW wind turbine. In addition, the ROSCO controller was incorporated into the model and the floating platform was modelled as a six degrees of freedom rigid body.
For the validation of the OpenFAST model, a comparison was performed with the on-site TetraSpar demonstrator. Data for the on-site demonstrator regarding motions, metocean conditions and power generation were provided by RWE. Further filtering and averaging the data in 10-minute periods for an entire year was performed. First, three one-hour simulations were performed for three different operating conditions: cut-in, below rated and above rated. Results from time series and power spectral density analyses showed a good agreement in platform motions. For the generated power, the mean average produced power was closest to the on-site data in the above rated region.
Next, for the power curve and AEP estimation, the probability of occurrence for each wind speed in the operating region was determined based on the on-site measured data. Furthermore, for every wind speed, the most representative significant wave height, wave period, turbulence intensity and other parameters needed as inputs in OpenFAST were found. The results showed that the AEP predicted by OpenFAST was 2.8% lower than the AEP measured at the on-site TetraSpar demonstrator. Moreover, different peak shaving levels of the controller contributed to the AEP percentage difference, ranging from about 4.4% to 2.1% compared to the on-site measurements. As far as platform motions are concerned, the model showed good agreement, capturing the trend of mean values and standard deviations of the two most dominant motions in power production, surge and pitch. In addition, the effect of wave height and wave period showed that the mean generated power is slightly affected. Similar results were also found for different current velocities. However, waves were found to influence the oscillation amplitudes of surge and pitch motions, while currents mainly affected the shift in mean values. Lastly, wind-wave misalignment also proved to have a negative effect on power performance.
In conclusion, the current OpenFAST model can be used to estimate the energy production and platform motion of the TetraSpar demonstrator. However, further improvements and reduction of the assumptions made, mainly due to the unavailability of data because of confidentiality, could enhance its accuracy.
Effect of structural elasticity on simulation of wind turbines and its verification
OpenFOAM based CFD simulations of HAWT coupled with FEM analysis based on spinning element
As wind turbines get bigger and bigger, the simulation of wind turbines becomes more complex. The increase in size brings about a multitude of intricate challenges that must be addressed in the simulation process. These challenges lie in the different aspects of the simulations such as aerodynamics, structural dynamics, power electronics, hydrodynamics, turbine control etc.
The central theme of this thesis is to explore the effect of one of the aspects- structural dynamics, on the wind turbine simulations. Moreover, the thesis also emphasises the development of a structural module and it’s integration in an OpenFOAM-based wind turbine simulation library called TurbinesFoam. TurbinesFoam is an actuator line method-based simulation tool, which enables the study of turbine performance as well as wake dynamics using Computational Fluid Dynamics. The key motivation is also to contribute towards the accurate simulation of wind turbines by performing a successful integration as said above.
To fulfil the goals, a structural module was developed in Matlab to simulate the
structural dynamics of wind turbines. The module is developed from the theory
of spinning elements assuming the blade and tower as Euler-Bernoulli beam. The developed module code is called while running the CFD simulations in OpenFOAM to add deflection and rotation of the blades and towers, using a MatLab pipe class.The accuracy of the developed code was measured using BModes and OpenFAST. The results exhibited satisfactory agreement between the outputs.
The NREL 5MW turbine was used to study the effects. The results revealed that,
at slightly above the rated condition, the turbine showed 1% decrease in power
production due to elasticity when compared with a rigid turbine. The thrust and
torque coefficients showed similar trends of reduction in value. Moreover, as the inflow wind velocity increased, the differences in performance got broader, due to increased deflection value. The wake region of the elastic turbine showed a mixed region of both increased and decreased wind velocity when compared with the rigid turbine.
...
The central theme of this thesis is to explore the effect of one of the aspects- structural dynamics, on the wind turbine simulations. Moreover, the thesis also emphasises the development of a structural module and it’s integration in an OpenFOAM-based wind turbine simulation library called TurbinesFoam. TurbinesFoam is an actuator line method-based simulation tool, which enables the study of turbine performance as well as wake dynamics using Computational Fluid Dynamics. The key motivation is also to contribute towards the accurate simulation of wind turbines by performing a successful integration as said above.
To fulfil the goals, a structural module was developed in Matlab to simulate the
structural dynamics of wind turbines. The module is developed from the theory
of spinning elements assuming the blade and tower as Euler-Bernoulli beam. The developed module code is called while running the CFD simulations in OpenFOAM to add deflection and rotation of the blades and towers, using a MatLab pipe class.The accuracy of the developed code was measured using BModes and OpenFAST. The results exhibited satisfactory agreement between the outputs.
The NREL 5MW turbine was used to study the effects. The results revealed that,
at slightly above the rated condition, the turbine showed 1% decrease in power
production due to elasticity when compared with a rigid turbine. The thrust and
torque coefficients showed similar trends of reduction in value. Moreover, as the inflow wind velocity increased, the differences in performance got broader, due to increased deflection value. The wake region of the elastic turbine showed a mixed region of both increased and decreased wind velocity when compared with the rigid turbine.
...
As wind turbines get bigger and bigger, the simulation of wind turbines becomes more complex. The increase in size brings about a multitude of intricate challenges that must be addressed in the simulation process. These challenges lie in the different aspects of the simulations such as aerodynamics, structural dynamics, power electronics, hydrodynamics, turbine control etc.
The central theme of this thesis is to explore the effect of one of the aspects- structural dynamics, on the wind turbine simulations. Moreover, the thesis also emphasises the development of a structural module and it’s integration in an OpenFOAM-based wind turbine simulation library called TurbinesFoam. TurbinesFoam is an actuator line method-based simulation tool, which enables the study of turbine performance as well as wake dynamics using Computational Fluid Dynamics. The key motivation is also to contribute towards the accurate simulation of wind turbines by performing a successful integration as said above.
To fulfil the goals, a structural module was developed in Matlab to simulate the
structural dynamics of wind turbines. The module is developed from the theory
of spinning elements assuming the blade and tower as Euler-Bernoulli beam. The developed module code is called while running the CFD simulations in OpenFOAM to add deflection and rotation of the blades and towers, using a MatLab pipe class.The accuracy of the developed code was measured using BModes and OpenFAST. The results exhibited satisfactory agreement between the outputs.
The NREL 5MW turbine was used to study the effects. The results revealed that,
at slightly above the rated condition, the turbine showed 1% decrease in power
production due to elasticity when compared with a rigid turbine. The thrust and
torque coefficients showed similar trends of reduction in value. Moreover, as the inflow wind velocity increased, the differences in performance got broader, due to increased deflection value. The wake region of the elastic turbine showed a mixed region of both increased and decreased wind velocity when compared with the rigid turbine.
The central theme of this thesis is to explore the effect of one of the aspects- structural dynamics, on the wind turbine simulations. Moreover, the thesis also emphasises the development of a structural module and it’s integration in an OpenFOAM-based wind turbine simulation library called TurbinesFoam. TurbinesFoam is an actuator line method-based simulation tool, which enables the study of turbine performance as well as wake dynamics using Computational Fluid Dynamics. The key motivation is also to contribute towards the accurate simulation of wind turbines by performing a successful integration as said above.
To fulfil the goals, a structural module was developed in Matlab to simulate the
structural dynamics of wind turbines. The module is developed from the theory
of spinning elements assuming the blade and tower as Euler-Bernoulli beam. The developed module code is called while running the CFD simulations in OpenFOAM to add deflection and rotation of the blades and towers, using a MatLab pipe class.The accuracy of the developed code was measured using BModes and OpenFAST. The results exhibited satisfactory agreement between the outputs.
The NREL 5MW turbine was used to study the effects. The results revealed that,
at slightly above the rated condition, the turbine showed 1% decrease in power
production due to elasticity when compared with a rigid turbine. The thrust and
torque coefficients showed similar trends of reduction in value. Moreover, as the inflow wind velocity increased, the differences in performance got broader, due to increased deflection value. The wake region of the elastic turbine showed a mixed region of both increased and decreased wind velocity when compared with the rigid turbine.
As the urge to decarbonise the energy field becomes increasingly important, a growing interest is shown in wind turbine technologies. In particular, the past few years have seen the development of floating offshore wind turbines for applications in deep waters.
In addition to the harsher environment they are facing, these wind turbines are mounted on floaters and therefore experience motions in the six additional degrees of freedom. As a result, this greatly alters their aerodynamic behaviour. The flow surrounding the rotor gains in complexity, becoming highly unsteady and three-dimensional. Thus, its resolution by numerical means calls for high-fidelity methods such as Large-Eddy Simulations (LES).
The objective of this study is to numerically impose single and coupled motions in the pitch and surge directions on a scale rotor of the DTU 10MW. This scale model was used for an experimental campaign within TU Delft, and particular interest is given to the comparison of the loads obtained.
Additionally, numerical simulations permit access to further information, such as the radial distribution of the loads or the wake development.
For this purpose, the LES code YALES 2 is used, implemented with an actuator line approach capable of dealing with imposed motions. Both 1-DOF and 2-DOF harmonic motions are imposed in the pitch and surge directions. Different reduced velocities and frequencies are considered.
On single-imposed surging motion, the loads are found to be well in accordance with the quasisteady theory (QST), even at high frequency, where much larger fluctuations were encountered during the experiment. Particular phenomena are also captured in the wake, such as the formation of vortex rings when the frequency is sufficiently high. Similar comments are made for 1-DOF pitching. Finally, for combined pitch/surge motions, the loads are also in accordance with the QST predictions made from the 1-DOF results. The influence of each motion on the wake’s development is also discussed.
Keywords: Floating offshore wind turbine (FOWT), Large-eddy simulation (LES), Actuator line method (ALM), Coupled imposed motions, pitch, surge ...
In addition to the harsher environment they are facing, these wind turbines are mounted on floaters and therefore experience motions in the six additional degrees of freedom. As a result, this greatly alters their aerodynamic behaviour. The flow surrounding the rotor gains in complexity, becoming highly unsteady and three-dimensional. Thus, its resolution by numerical means calls for high-fidelity methods such as Large-Eddy Simulations (LES).
The objective of this study is to numerically impose single and coupled motions in the pitch and surge directions on a scale rotor of the DTU 10MW. This scale model was used for an experimental campaign within TU Delft, and particular interest is given to the comparison of the loads obtained.
Additionally, numerical simulations permit access to further information, such as the radial distribution of the loads or the wake development.
For this purpose, the LES code YALES 2 is used, implemented with an actuator line approach capable of dealing with imposed motions. Both 1-DOF and 2-DOF harmonic motions are imposed in the pitch and surge directions. Different reduced velocities and frequencies are considered.
On single-imposed surging motion, the loads are found to be well in accordance with the quasisteady theory (QST), even at high frequency, where much larger fluctuations were encountered during the experiment. Particular phenomena are also captured in the wake, such as the formation of vortex rings when the frequency is sufficiently high. Similar comments are made for 1-DOF pitching. Finally, for combined pitch/surge motions, the loads are also in accordance with the QST predictions made from the 1-DOF results. The influence of each motion on the wake’s development is also discussed.
Keywords: Floating offshore wind turbine (FOWT), Large-eddy simulation (LES), Actuator line method (ALM), Coupled imposed motions, pitch, surge ...
As the urge to decarbonise the energy field becomes increasingly important, a growing interest is shown in wind turbine technologies. In particular, the past few years have seen the development of floating offshore wind turbines for applications in deep waters.
In addition to the harsher environment they are facing, these wind turbines are mounted on floaters and therefore experience motions in the six additional degrees of freedom. As a result, this greatly alters their aerodynamic behaviour. The flow surrounding the rotor gains in complexity, becoming highly unsteady and three-dimensional. Thus, its resolution by numerical means calls for high-fidelity methods such as Large-Eddy Simulations (LES).
The objective of this study is to numerically impose single and coupled motions in the pitch and surge directions on a scale rotor of the DTU 10MW. This scale model was used for an experimental campaign within TU Delft, and particular interest is given to the comparison of the loads obtained.
Additionally, numerical simulations permit access to further information, such as the radial distribution of the loads or the wake development.
For this purpose, the LES code YALES 2 is used, implemented with an actuator line approach capable of dealing with imposed motions. Both 1-DOF and 2-DOF harmonic motions are imposed in the pitch and surge directions. Different reduced velocities and frequencies are considered.
On single-imposed surging motion, the loads are found to be well in accordance with the quasisteady theory (QST), even at high frequency, where much larger fluctuations were encountered during the experiment. Particular phenomena are also captured in the wake, such as the formation of vortex rings when the frequency is sufficiently high. Similar comments are made for 1-DOF pitching. Finally, for combined pitch/surge motions, the loads are also in accordance with the QST predictions made from the 1-DOF results. The influence of each motion on the wake’s development is also discussed.
Keywords: Floating offshore wind turbine (FOWT), Large-eddy simulation (LES), Actuator line method (ALM), Coupled imposed motions, pitch, surge
In addition to the harsher environment they are facing, these wind turbines are mounted on floaters and therefore experience motions in the six additional degrees of freedom. As a result, this greatly alters their aerodynamic behaviour. The flow surrounding the rotor gains in complexity, becoming highly unsteady and three-dimensional. Thus, its resolution by numerical means calls for high-fidelity methods such as Large-Eddy Simulations (LES).
The objective of this study is to numerically impose single and coupled motions in the pitch and surge directions on a scale rotor of the DTU 10MW. This scale model was used for an experimental campaign within TU Delft, and particular interest is given to the comparison of the loads obtained.
Additionally, numerical simulations permit access to further information, such as the radial distribution of the loads or the wake development.
For this purpose, the LES code YALES 2 is used, implemented with an actuator line approach capable of dealing with imposed motions. Both 1-DOF and 2-DOF harmonic motions are imposed in the pitch and surge directions. Different reduced velocities and frequencies are considered.
On single-imposed surging motion, the loads are found to be well in accordance with the quasisteady theory (QST), even at high frequency, where much larger fluctuations were encountered during the experiment. Particular phenomena are also captured in the wake, such as the formation of vortex rings when the frequency is sufficiently high. Similar comments are made for 1-DOF pitching. Finally, for combined pitch/surge motions, the loads are also in accordance with the QST predictions made from the 1-DOF results. The influence of each motion on the wake’s development is also discussed.
Keywords: Floating offshore wind turbine (FOWT), Large-eddy simulation (LES), Actuator line method (ALM), Coupled imposed motions, pitch, surge
This thesis project titled “Routing Methods and Design Considerations for Unmoored Floating Offshore Wind Turbine Concepts” investigates the emerging technological deployment of Unmoored Floating Offshore Wind Turbines (UFOWTs), which are operated via autonomous routing to navigate their constantly changing wind and generation sites. This form of wind energy conversion offers the opportunity for wind energy harnessing further offshore and in deeper water sites than currently possible, along with theoretically shorter deployment times. These two advantages, along with others identified for this technology, aid the upcoming 2050 goals of the Net-Zero Coalition for renewable energy generation.
The current prevailing research focus on UFOWT systems in literature is concept development and modelling to demonstrate pre-feasibility in terms of stability, costs, and energy balance. This thesis aims to evaluate two prevailing routing methods and system designs of UFOWT concepts and to investigate how operational methods, design choices, and site conditions interact with one another to affect UFOWT performance.
The research workflow of this project can be divided into three processes. The first involved a literature review that provided the author with the necessary understanding of existing work in the realm of UFOWTs, which provided guidance for the research question formulation. This first research process blended into the second, which involved defining two separate UFOWT designs to investigate in this project, each with their individual routing method: Alternating Navigation and Generation (ANG) and Continuous Navigation and Generation (CNG) methods. The system modelling scope was defined in this step, setting assumptions for aspects that could not be covered in the scope of work. The third process involved building the simulation environment for the UFOWTs from the ground up, done in MATLAB (R2021a version) using weather data from the Copernicus ERA5 open-source database.
The results of this project were simulated routes and energy yields for operation of each UFOWT design in the North Sea, West, and Eastern Mediterranean Sea regions. Using the same UFOWT design in all regions showed poor performance in lower average wind speed areas, due to lower generation and increased costs associated with further distances travelled and dictated by the routing algorithm. Over the period of 10 years for which data was available to simulate both systems, it was found that there are additional considerations to be made for UFOWT operation which influence overall performance, for instance starting location and wind speed direction trends. Operation of the two UFOWT designs was simulated in 3-month intervals from 2012 to 2021, which yielded an average performance of the ANG system nearly twice as high as the CNG system, in terms of capacity factor and simulated onboard fuel conversion. ...
The current prevailing research focus on UFOWT systems in literature is concept development and modelling to demonstrate pre-feasibility in terms of stability, costs, and energy balance. This thesis aims to evaluate two prevailing routing methods and system designs of UFOWT concepts and to investigate how operational methods, design choices, and site conditions interact with one another to affect UFOWT performance.
The research workflow of this project can be divided into three processes. The first involved a literature review that provided the author with the necessary understanding of existing work in the realm of UFOWTs, which provided guidance for the research question formulation. This first research process blended into the second, which involved defining two separate UFOWT designs to investigate in this project, each with their individual routing method: Alternating Navigation and Generation (ANG) and Continuous Navigation and Generation (CNG) methods. The system modelling scope was defined in this step, setting assumptions for aspects that could not be covered in the scope of work. The third process involved building the simulation environment for the UFOWTs from the ground up, done in MATLAB (R2021a version) using weather data from the Copernicus ERA5 open-source database.
The results of this project were simulated routes and energy yields for operation of each UFOWT design in the North Sea, West, and Eastern Mediterranean Sea regions. Using the same UFOWT design in all regions showed poor performance in lower average wind speed areas, due to lower generation and increased costs associated with further distances travelled and dictated by the routing algorithm. Over the period of 10 years for which data was available to simulate both systems, it was found that there are additional considerations to be made for UFOWT operation which influence overall performance, for instance starting location and wind speed direction trends. Operation of the two UFOWT designs was simulated in 3-month intervals from 2012 to 2021, which yielded an average performance of the ANG system nearly twice as high as the CNG system, in terms of capacity factor and simulated onboard fuel conversion. ...
This thesis project titled “Routing Methods and Design Considerations for Unmoored Floating Offshore Wind Turbine Concepts” investigates the emerging technological deployment of Unmoored Floating Offshore Wind Turbines (UFOWTs), which are operated via autonomous routing to navigate their constantly changing wind and generation sites. This form of wind energy conversion offers the opportunity for wind energy harnessing further offshore and in deeper water sites than currently possible, along with theoretically shorter deployment times. These two advantages, along with others identified for this technology, aid the upcoming 2050 goals of the Net-Zero Coalition for renewable energy generation.
The current prevailing research focus on UFOWT systems in literature is concept development and modelling to demonstrate pre-feasibility in terms of stability, costs, and energy balance. This thesis aims to evaluate two prevailing routing methods and system designs of UFOWT concepts and to investigate how operational methods, design choices, and site conditions interact with one another to affect UFOWT performance.
The research workflow of this project can be divided into three processes. The first involved a literature review that provided the author with the necessary understanding of existing work in the realm of UFOWTs, which provided guidance for the research question formulation. This first research process blended into the second, which involved defining two separate UFOWT designs to investigate in this project, each with their individual routing method: Alternating Navigation and Generation (ANG) and Continuous Navigation and Generation (CNG) methods. The system modelling scope was defined in this step, setting assumptions for aspects that could not be covered in the scope of work. The third process involved building the simulation environment for the UFOWTs from the ground up, done in MATLAB (R2021a version) using weather data from the Copernicus ERA5 open-source database.
The results of this project were simulated routes and energy yields for operation of each UFOWT design in the North Sea, West, and Eastern Mediterranean Sea regions. Using the same UFOWT design in all regions showed poor performance in lower average wind speed areas, due to lower generation and increased costs associated with further distances travelled and dictated by the routing algorithm. Over the period of 10 years for which data was available to simulate both systems, it was found that there are additional considerations to be made for UFOWT operation which influence overall performance, for instance starting location and wind speed direction trends. Operation of the two UFOWT designs was simulated in 3-month intervals from 2012 to 2021, which yielded an average performance of the ANG system nearly twice as high as the CNG system, in terms of capacity factor and simulated onboard fuel conversion.
The current prevailing research focus on UFOWT systems in literature is concept development and modelling to demonstrate pre-feasibility in terms of stability, costs, and energy balance. This thesis aims to evaluate two prevailing routing methods and system designs of UFOWT concepts and to investigate how operational methods, design choices, and site conditions interact with one another to affect UFOWT performance.
The research workflow of this project can be divided into three processes. The first involved a literature review that provided the author with the necessary understanding of existing work in the realm of UFOWTs, which provided guidance for the research question formulation. This first research process blended into the second, which involved defining two separate UFOWT designs to investigate in this project, each with their individual routing method: Alternating Navigation and Generation (ANG) and Continuous Navigation and Generation (CNG) methods. The system modelling scope was defined in this step, setting assumptions for aspects that could not be covered in the scope of work. The third process involved building the simulation environment for the UFOWTs from the ground up, done in MATLAB (R2021a version) using weather data from the Copernicus ERA5 open-source database.
The results of this project were simulated routes and energy yields for operation of each UFOWT design in the North Sea, West, and Eastern Mediterranean Sea regions. Using the same UFOWT design in all regions showed poor performance in lower average wind speed areas, due to lower generation and increased costs associated with further distances travelled and dictated by the routing algorithm. Over the period of 10 years for which data was available to simulate both systems, it was found that there are additional considerations to be made for UFOWT operation which influence overall performance, for instance starting location and wind speed direction trends. Operation of the two UFOWT designs was simulated in 3-month intervals from 2012 to 2021, which yielded an average performance of the ANG system nearly twice as high as the CNG system, in terms of capacity factor and simulated onboard fuel conversion.
In recent years, the interest in deep-water wind energy projects has drastically increased, driven by the considerable wind resource in deep-water locations. In such locations, floating wind turbines are more economically attractive than bottom-fixed wind turbines. Nevertheless, the floating wind industry faces numerous challenges. One major challenge is the high investment costs involved in the manufacturing of the substructure. Additionally, it is important to maintain the wind turbines’ power output within the required standards for grid connection. Therefore, the design and optimization of substructures for floating wind turbines has to account for both capital costs and power quality. To investigate the existing research on the optimization of floating wind turbines, a literature review was conducted. Evidently, there is limited research investigating both cost and power quality optimization. Furthermore, the majority of the optimization techniques presented are accompanied by a computationally expensive substructure analysis. Moreover, in the early stage of the substructure design, a variety of different alternative scenarios are tested, requiring an extensive number of optimizations. Thus, the computational resources required by the optimization become a major concern. One promising approach to address this issue is substituting a computationally expensive analysis with a surrogate model. This solution could significantly increase the computational efficiency of the optimization process.
This thesis investigates the trade-off between substructure capital cost and power quality optimization. To achieve that, a multi-objective optimization workflow is developed for the case of a semi-submersible substructure. In parallel with this task, the optimization’s analysis blocks requiring most of the computational resources are identified and substituted with a surrogate model. Two different surrogate model implementation approaches have been examined. The first approach substitutes the optimization’s substructure analysis tool (NREL’s RAFT) entirely. This approach is called the ”End-to-End” approach. The second approach substitutes a specific analysis block of RAFT that computes the floater’s hydrodynamic properties. This analysis block requires most of RAFT’s computational resources. This approach is defined as the ”PyHAMS” approach.
The thesis outcome shows that improving the quality of power output comes with the drawback of increasing the substructure’s cost. The Pareto front between these two competing criteria is dominated by designs of long pontoons and reduced chain thickness. This is the most economical approach towards reduced cost and improved power quality. These Pareto designs are mainly driven by the boundaries of the design variables. Additionally, the Pareto designs are affected by constraints on the maximum pitch, maximum surge, and the existence of vertical loads on the mooring line’s anchor. Regarding the surrogate model-based optimizations, there is a dramatic reduction in the computational resources required to conduct the optimization. Among the two surrogate model approaches, the ”PyHAMS” approach achieves a better accuracy at mimicking the original optimization workflow than the ”End-to-End”. Instead, the ”End-to-End” approach is the most computationally efficient one to conduct the optimization. Nevertheless, when factoring in the computational resources to produce the training dataset, the ”PyHAMS” approach is the most computationally efficient one. This is the outcome of utilizing a reduced training dataset compared to one utilized by the ”End-to- End” approach. ...
This thesis investigates the trade-off between substructure capital cost and power quality optimization. To achieve that, a multi-objective optimization workflow is developed for the case of a semi-submersible substructure. In parallel with this task, the optimization’s analysis blocks requiring most of the computational resources are identified and substituted with a surrogate model. Two different surrogate model implementation approaches have been examined. The first approach substitutes the optimization’s substructure analysis tool (NREL’s RAFT) entirely. This approach is called the ”End-to-End” approach. The second approach substitutes a specific analysis block of RAFT that computes the floater’s hydrodynamic properties. This analysis block requires most of RAFT’s computational resources. This approach is defined as the ”PyHAMS” approach.
The thesis outcome shows that improving the quality of power output comes with the drawback of increasing the substructure’s cost. The Pareto front between these two competing criteria is dominated by designs of long pontoons and reduced chain thickness. This is the most economical approach towards reduced cost and improved power quality. These Pareto designs are mainly driven by the boundaries of the design variables. Additionally, the Pareto designs are affected by constraints on the maximum pitch, maximum surge, and the existence of vertical loads on the mooring line’s anchor. Regarding the surrogate model-based optimizations, there is a dramatic reduction in the computational resources required to conduct the optimization. Among the two surrogate model approaches, the ”PyHAMS” approach achieves a better accuracy at mimicking the original optimization workflow than the ”End-to-End”. Instead, the ”End-to-End” approach is the most computationally efficient one to conduct the optimization. Nevertheless, when factoring in the computational resources to produce the training dataset, the ”PyHAMS” approach is the most computationally efficient one. This is the outcome of utilizing a reduced training dataset compared to one utilized by the ”End-to- End” approach. ...
In recent years, the interest in deep-water wind energy projects has drastically increased, driven by the considerable wind resource in deep-water locations. In such locations, floating wind turbines are more economically attractive than bottom-fixed wind turbines. Nevertheless, the floating wind industry faces numerous challenges. One major challenge is the high investment costs involved in the manufacturing of the substructure. Additionally, it is important to maintain the wind turbines’ power output within the required standards for grid connection. Therefore, the design and optimization of substructures for floating wind turbines has to account for both capital costs and power quality. To investigate the existing research on the optimization of floating wind turbines, a literature review was conducted. Evidently, there is limited research investigating both cost and power quality optimization. Furthermore, the majority of the optimization techniques presented are accompanied by a computationally expensive substructure analysis. Moreover, in the early stage of the substructure design, a variety of different alternative scenarios are tested, requiring an extensive number of optimizations. Thus, the computational resources required by the optimization become a major concern. One promising approach to address this issue is substituting a computationally expensive analysis with a surrogate model. This solution could significantly increase the computational efficiency of the optimization process.
This thesis investigates the trade-off between substructure capital cost and power quality optimization. To achieve that, a multi-objective optimization workflow is developed for the case of a semi-submersible substructure. In parallel with this task, the optimization’s analysis blocks requiring most of the computational resources are identified and substituted with a surrogate model. Two different surrogate model implementation approaches have been examined. The first approach substitutes the optimization’s substructure analysis tool (NREL’s RAFT) entirely. This approach is called the ”End-to-End” approach. The second approach substitutes a specific analysis block of RAFT that computes the floater’s hydrodynamic properties. This analysis block requires most of RAFT’s computational resources. This approach is defined as the ”PyHAMS” approach.
The thesis outcome shows that improving the quality of power output comes with the drawback of increasing the substructure’s cost. The Pareto front between these two competing criteria is dominated by designs of long pontoons and reduced chain thickness. This is the most economical approach towards reduced cost and improved power quality. These Pareto designs are mainly driven by the boundaries of the design variables. Additionally, the Pareto designs are affected by constraints on the maximum pitch, maximum surge, and the existence of vertical loads on the mooring line’s anchor. Regarding the surrogate model-based optimizations, there is a dramatic reduction in the computational resources required to conduct the optimization. Among the two surrogate model approaches, the ”PyHAMS” approach achieves a better accuracy at mimicking the original optimization workflow than the ”End-to-End”. Instead, the ”End-to-End” approach is the most computationally efficient one to conduct the optimization. Nevertheless, when factoring in the computational resources to produce the training dataset, the ”PyHAMS” approach is the most computationally efficient one. This is the outcome of utilizing a reduced training dataset compared to one utilized by the ”End-to- End” approach.
This thesis investigates the trade-off between substructure capital cost and power quality optimization. To achieve that, a multi-objective optimization workflow is developed for the case of a semi-submersible substructure. In parallel with this task, the optimization’s analysis blocks requiring most of the computational resources are identified and substituted with a surrogate model. Two different surrogate model implementation approaches have been examined. The first approach substitutes the optimization’s substructure analysis tool (NREL’s RAFT) entirely. This approach is called the ”End-to-End” approach. The second approach substitutes a specific analysis block of RAFT that computes the floater’s hydrodynamic properties. This analysis block requires most of RAFT’s computational resources. This approach is defined as the ”PyHAMS” approach.
The thesis outcome shows that improving the quality of power output comes with the drawback of increasing the substructure’s cost. The Pareto front between these two competing criteria is dominated by designs of long pontoons and reduced chain thickness. This is the most economical approach towards reduced cost and improved power quality. These Pareto designs are mainly driven by the boundaries of the design variables. Additionally, the Pareto designs are affected by constraints on the maximum pitch, maximum surge, and the existence of vertical loads on the mooring line’s anchor. Regarding the surrogate model-based optimizations, there is a dramatic reduction in the computational resources required to conduct the optimization. Among the two surrogate model approaches, the ”PyHAMS” approach achieves a better accuracy at mimicking the original optimization workflow than the ”End-to-End”. Instead, the ”End-to-End” approach is the most computationally efficient one to conduct the optimization. Nevertheless, when factoring in the computational resources to produce the training dataset, the ”PyHAMS” approach is the most computationally efficient one. This is the outcome of utilizing a reduced training dataset compared to one utilized by the ”End-to- End” approach.
The purpose of this study has been to develop an aerodynamic load model for the energy harvesting (traction) phase of leading-edge inflatable (LEI) kites operating in an airborne wind energy (AWE) context. The load model stems from multivariate polynomial regression analyses expressing the airfoil lift, drag and moment coefficients as polynomial functions of the angle-of-attack and 2D non-dimensional (relative to the chord length) shape parameters: non-dimensional tube diameter, maximum camber magnitude and chordwise position of maximum camber. The regression analyses relied on numerical data attained from computational fluid dynamics (CFD) simulations of the 2D flow fields around parameterised LEI wing profiles. The RANS equations, closed by the k-ω SST turbulence model, have been used for this purpose. The parameterisation and subsequent geometric construction of LEI wing profiles has been a key aspect of this study. As such, the effects of the shape parameters on the flow field have been assessed.
...
The purpose of this study has been to develop an aerodynamic load model for the energy harvesting (traction) phase of leading-edge inflatable (LEI) kites operating in an airborne wind energy (AWE) context. The load model stems from multivariate polynomial regression analyses expressing the airfoil lift, drag and moment coefficients as polynomial functions of the angle-of-attack and 2D non-dimensional (relative to the chord length) shape parameters: non-dimensional tube diameter, maximum camber magnitude and chordwise position of maximum camber. The regression analyses relied on numerical data attained from computational fluid dynamics (CFD) simulations of the 2D flow fields around parameterised LEI wing profiles. The RANS equations, closed by the k-ω SST turbulence model, have been used for this purpose. The parameterisation and subsequent geometric construction of LEI wing profiles has been a key aspect of this study. As such, the effects of the shape parameters on the flow field have been assessed.
Wake Effect Mitigation of Floating Offshore Wind Farms
Combining Layout Optimization, Turbine Repositioning and Yaw-based Wake Redirection
Floating wind turbines despite the the potential to harness energy from deep offshore areas where higher average wind speeds face challenges in terms of competitiveness. One approach to raising the competitiveness of a wind farm is to mitigate efficiency losses resulting from the wake effect. This report focuses on the combination of three notable wake effect mitigation strategies: layout optimization, yaw-based wake redirection, and turbine repositioning.
A preliminary analysis of the combined effect of wind turbine repositioning and yaw-based wake redirection on power performance for the case of two turbines only is performed. Above rated wind speeds, upstream turbine yawing reduces downstream turbine movement, with reductions of around 1 to 4 rotor diameters longitudinally and 0.2 to 0.5 times the rotor diameter laterally, keeping the same level of power efficiency.
Nextly, an optimization problem that integrate layout optimization with yaw-based wake steering and turbine repositioning for power maximization across an extended wind farm is formulated. The optimization frame followed a sequential approach. The results on a case study confirmed that the effect of adding yaw-based wake redirection to turbine repositioning remains significant for multiple turbines, with several percent-point efficiency improvements for small movable ranges. For larger ranges, the contribution of yaw control diminishes rapidly to one percent-point or less. Yaw control enables movable range reductions of 10% to 50%, preserving wind farm efficiency. Yet, reductions are more pronounced in smaller, less effective movable ranges. Below rated conditions, the effectiveness of yaw control diminishes swiftly.
Furthermore, the study delves into the implications of integrating position mooring for turbine repositioning and yaw-based wake mitigation strategies on mooring system performance. This examination employs a proposed methodology aimed at minimizing the position error across most points within the movable range Both the tension of the mooring lines and the static stiffness of the floater showed to be sensitive to the position of floater, the direction of the wind load and the yaw of the wind turbine, with percentual changes ranging from 0.5% up to 50%. It results that the orientation of the mooring lines with correspondence of the prevailing wind direction, as well as that restrictive constraints on the tension and stiffness may be taken should be taken into account when designing a mooring system for turbine repositioning.
Overall, combining , yaw-based wake redirection, and turbine repositioning allows for greater wind farm AEP, with gains contingent on turbine movable range and upcoming wind speeds. Designing position mooring systems must factor in the influence of yaw-based wake redirection and turbine repositioning on mooring system tension and stiffness. More advanced analyses, including dynamic assessments, are essential for comprehending the mooring lines system's response to position mooring for turbine repositioning, and yaw-based wake redirection.
...
A preliminary analysis of the combined effect of wind turbine repositioning and yaw-based wake redirection on power performance for the case of two turbines only is performed. Above rated wind speeds, upstream turbine yawing reduces downstream turbine movement, with reductions of around 1 to 4 rotor diameters longitudinally and 0.2 to 0.5 times the rotor diameter laterally, keeping the same level of power efficiency.
Nextly, an optimization problem that integrate layout optimization with yaw-based wake steering and turbine repositioning for power maximization across an extended wind farm is formulated. The optimization frame followed a sequential approach. The results on a case study confirmed that the effect of adding yaw-based wake redirection to turbine repositioning remains significant for multiple turbines, with several percent-point efficiency improvements for small movable ranges. For larger ranges, the contribution of yaw control diminishes rapidly to one percent-point or less. Yaw control enables movable range reductions of 10% to 50%, preserving wind farm efficiency. Yet, reductions are more pronounced in smaller, less effective movable ranges. Below rated conditions, the effectiveness of yaw control diminishes swiftly.
Furthermore, the study delves into the implications of integrating position mooring for turbine repositioning and yaw-based wake mitigation strategies on mooring system performance. This examination employs a proposed methodology aimed at minimizing the position error across most points within the movable range Both the tension of the mooring lines and the static stiffness of the floater showed to be sensitive to the position of floater, the direction of the wind load and the yaw of the wind turbine, with percentual changes ranging from 0.5% up to 50%. It results that the orientation of the mooring lines with correspondence of the prevailing wind direction, as well as that restrictive constraints on the tension and stiffness may be taken should be taken into account when designing a mooring system for turbine repositioning.
Overall, combining , yaw-based wake redirection, and turbine repositioning allows for greater wind farm AEP, with gains contingent on turbine movable range and upcoming wind speeds. Designing position mooring systems must factor in the influence of yaw-based wake redirection and turbine repositioning on mooring system tension and stiffness. More advanced analyses, including dynamic assessments, are essential for comprehending the mooring lines system's response to position mooring for turbine repositioning, and yaw-based wake redirection.
...
Floating wind turbines despite the the potential to harness energy from deep offshore areas where higher average wind speeds face challenges in terms of competitiveness. One approach to raising the competitiveness of a wind farm is to mitigate efficiency losses resulting from the wake effect. This report focuses on the combination of three notable wake effect mitigation strategies: layout optimization, yaw-based wake redirection, and turbine repositioning.
A preliminary analysis of the combined effect of wind turbine repositioning and yaw-based wake redirection on power performance for the case of two turbines only is performed. Above rated wind speeds, upstream turbine yawing reduces downstream turbine movement, with reductions of around 1 to 4 rotor diameters longitudinally and 0.2 to 0.5 times the rotor diameter laterally, keeping the same level of power efficiency.
Nextly, an optimization problem that integrate layout optimization with yaw-based wake steering and turbine repositioning for power maximization across an extended wind farm is formulated. The optimization frame followed a sequential approach. The results on a case study confirmed that the effect of adding yaw-based wake redirection to turbine repositioning remains significant for multiple turbines, with several percent-point efficiency improvements for small movable ranges. For larger ranges, the contribution of yaw control diminishes rapidly to one percent-point or less. Yaw control enables movable range reductions of 10% to 50%, preserving wind farm efficiency. Yet, reductions are more pronounced in smaller, less effective movable ranges. Below rated conditions, the effectiveness of yaw control diminishes swiftly.
Furthermore, the study delves into the implications of integrating position mooring for turbine repositioning and yaw-based wake mitigation strategies on mooring system performance. This examination employs a proposed methodology aimed at minimizing the position error across most points within the movable range Both the tension of the mooring lines and the static stiffness of the floater showed to be sensitive to the position of floater, the direction of the wind load and the yaw of the wind turbine, with percentual changes ranging from 0.5% up to 50%. It results that the orientation of the mooring lines with correspondence of the prevailing wind direction, as well as that restrictive constraints on the tension and stiffness may be taken should be taken into account when designing a mooring system for turbine repositioning.
Overall, combining , yaw-based wake redirection, and turbine repositioning allows for greater wind farm AEP, with gains contingent on turbine movable range and upcoming wind speeds. Designing position mooring systems must factor in the influence of yaw-based wake redirection and turbine repositioning on mooring system tension and stiffness. More advanced analyses, including dynamic assessments, are essential for comprehending the mooring lines system's response to position mooring for turbine repositioning, and yaw-based wake redirection.
A preliminary analysis of the combined effect of wind turbine repositioning and yaw-based wake redirection on power performance for the case of two turbines only is performed. Above rated wind speeds, upstream turbine yawing reduces downstream turbine movement, with reductions of around 1 to 4 rotor diameters longitudinally and 0.2 to 0.5 times the rotor diameter laterally, keeping the same level of power efficiency.
Nextly, an optimization problem that integrate layout optimization with yaw-based wake steering and turbine repositioning for power maximization across an extended wind farm is formulated. The optimization frame followed a sequential approach. The results on a case study confirmed that the effect of adding yaw-based wake redirection to turbine repositioning remains significant for multiple turbines, with several percent-point efficiency improvements for small movable ranges. For larger ranges, the contribution of yaw control diminishes rapidly to one percent-point or less. Yaw control enables movable range reductions of 10% to 50%, preserving wind farm efficiency. Yet, reductions are more pronounced in smaller, less effective movable ranges. Below rated conditions, the effectiveness of yaw control diminishes swiftly.
Furthermore, the study delves into the implications of integrating position mooring for turbine repositioning and yaw-based wake mitigation strategies on mooring system performance. This examination employs a proposed methodology aimed at minimizing the position error across most points within the movable range Both the tension of the mooring lines and the static stiffness of the floater showed to be sensitive to the position of floater, the direction of the wind load and the yaw of the wind turbine, with percentual changes ranging from 0.5% up to 50%. It results that the orientation of the mooring lines with correspondence of the prevailing wind direction, as well as that restrictive constraints on the tension and stiffness may be taken should be taken into account when designing a mooring system for turbine repositioning.
Overall, combining , yaw-based wake redirection, and turbine repositioning allows for greater wind farm AEP, with gains contingent on turbine movable range and upcoming wind speeds. Designing position mooring systems must factor in the influence of yaw-based wake redirection and turbine repositioning on mooring system tension and stiffness. More advanced analyses, including dynamic assessments, are essential for comprehending the mooring lines system's response to position mooring for turbine repositioning, and yaw-based wake redirection.
Considering the goals set by the international community, the implementation of new energy sources has to increase considerably in the next seven years. In this thesis, the focus is on the acceleration and improvement of the application of offshore wind turbines. The power produced using this technology should become 3.6 times more before the end of this decade to comply with the set goals.
To achieve this target, new solutions have to be developed. To this aim, the implementation of model predictive control for wind turbines in the last years has been investigated. This would allow to optimize different parameters at the same time, such as maximization of energy production while minimizing the perceived loads. For this application, it is necessary to have forecasts of different features of the turbine, especially loads, with a higher frequency. As a result, the main research topic is defined as 'Can data-driven surrogate models be used for forecasting load time series on offshore wind turbines?'.
To answer this question, first environmental conditions are sampled within limits deducted from real data through Halton sequencing, and next simulations are run through OpenFAST to determine the resulting loads acting on the turbine. Within all the features resulting from the simulation, only five inputs and five target outputs are selected. This is the result of various considerations. Given the desire to develop a realistic methodology, the input variables are first filtered by assessing their availability from measurement devices. Next, the relationships between the variables are analyzed through cross-correlation to determine the degree of influence of each input on the output.
Using this data, a training database is created. It is used to train two different types of surrogate models, one linear and one non-linear, respectively ARIMAX and LSTM. These are implemented to generate a 30-second forecast of the moments acting at the root of the blade. To do so, the algorithms are trained using different variables as exogenous inputs to assess the models' performance in different cases. Given the wide range of target features, for LSTM two different behaviors are identified and the blade edgewise and flapwise moments are taken as examples. The hyper-parameters are tuned on the blade edgewise moment and lead to overfitting when applied to the blade flapwise and out-of-plane moment.
The obtained results show that the RMSE in ARIMAX is up to seven times larger than the one obtained from the application of LSTM. Within the non-linear models, the one resulting in the lowest percentage error for the blade edgewise, pitching, and in-plane moment considers the wind reference speed, the wind speed time series, and the corresponding tip deflection as exogenous inputs. Very low RMSE errors are obtained for all variables. Furthermore, it is concluded that while it is possible to implement LSTM in real-life, this is not achievable for ARIMAX. ...
To achieve this target, new solutions have to be developed. To this aim, the implementation of model predictive control for wind turbines in the last years has been investigated. This would allow to optimize different parameters at the same time, such as maximization of energy production while minimizing the perceived loads. For this application, it is necessary to have forecasts of different features of the turbine, especially loads, with a higher frequency. As a result, the main research topic is defined as 'Can data-driven surrogate models be used for forecasting load time series on offshore wind turbines?'.
To answer this question, first environmental conditions are sampled within limits deducted from real data through Halton sequencing, and next simulations are run through OpenFAST to determine the resulting loads acting on the turbine. Within all the features resulting from the simulation, only five inputs and five target outputs are selected. This is the result of various considerations. Given the desire to develop a realistic methodology, the input variables are first filtered by assessing their availability from measurement devices. Next, the relationships between the variables are analyzed through cross-correlation to determine the degree of influence of each input on the output.
Using this data, a training database is created. It is used to train two different types of surrogate models, one linear and one non-linear, respectively ARIMAX and LSTM. These are implemented to generate a 30-second forecast of the moments acting at the root of the blade. To do so, the algorithms are trained using different variables as exogenous inputs to assess the models' performance in different cases. Given the wide range of target features, for LSTM two different behaviors are identified and the blade edgewise and flapwise moments are taken as examples. The hyper-parameters are tuned on the blade edgewise moment and lead to overfitting when applied to the blade flapwise and out-of-plane moment.
The obtained results show that the RMSE in ARIMAX is up to seven times larger than the one obtained from the application of LSTM. Within the non-linear models, the one resulting in the lowest percentage error for the blade edgewise, pitching, and in-plane moment considers the wind reference speed, the wind speed time series, and the corresponding tip deflection as exogenous inputs. Very low RMSE errors are obtained for all variables. Furthermore, it is concluded that while it is possible to implement LSTM in real-life, this is not achievable for ARIMAX. ...
Considering the goals set by the international community, the implementation of new energy sources has to increase considerably in the next seven years. In this thesis, the focus is on the acceleration and improvement of the application of offshore wind turbines. The power produced using this technology should become 3.6 times more before the end of this decade to comply with the set goals.
To achieve this target, new solutions have to be developed. To this aim, the implementation of model predictive control for wind turbines in the last years has been investigated. This would allow to optimize different parameters at the same time, such as maximization of energy production while minimizing the perceived loads. For this application, it is necessary to have forecasts of different features of the turbine, especially loads, with a higher frequency. As a result, the main research topic is defined as 'Can data-driven surrogate models be used for forecasting load time series on offshore wind turbines?'.
To answer this question, first environmental conditions are sampled within limits deducted from real data through Halton sequencing, and next simulations are run through OpenFAST to determine the resulting loads acting on the turbine. Within all the features resulting from the simulation, only five inputs and five target outputs are selected. This is the result of various considerations. Given the desire to develop a realistic methodology, the input variables are first filtered by assessing their availability from measurement devices. Next, the relationships between the variables are analyzed through cross-correlation to determine the degree of influence of each input on the output.
Using this data, a training database is created. It is used to train two different types of surrogate models, one linear and one non-linear, respectively ARIMAX and LSTM. These are implemented to generate a 30-second forecast of the moments acting at the root of the blade. To do so, the algorithms are trained using different variables as exogenous inputs to assess the models' performance in different cases. Given the wide range of target features, for LSTM two different behaviors are identified and the blade edgewise and flapwise moments are taken as examples. The hyper-parameters are tuned on the blade edgewise moment and lead to overfitting when applied to the blade flapwise and out-of-plane moment.
The obtained results show that the RMSE in ARIMAX is up to seven times larger than the one obtained from the application of LSTM. Within the non-linear models, the one resulting in the lowest percentage error for the blade edgewise, pitching, and in-plane moment considers the wind reference speed, the wind speed time series, and the corresponding tip deflection as exogenous inputs. Very low RMSE errors are obtained for all variables. Furthermore, it is concluded that while it is possible to implement LSTM in real-life, this is not achievable for ARIMAX.
To achieve this target, new solutions have to be developed. To this aim, the implementation of model predictive control for wind turbines in the last years has been investigated. This would allow to optimize different parameters at the same time, such as maximization of energy production while minimizing the perceived loads. For this application, it is necessary to have forecasts of different features of the turbine, especially loads, with a higher frequency. As a result, the main research topic is defined as 'Can data-driven surrogate models be used for forecasting load time series on offshore wind turbines?'.
To answer this question, first environmental conditions are sampled within limits deducted from real data through Halton sequencing, and next simulations are run through OpenFAST to determine the resulting loads acting on the turbine. Within all the features resulting from the simulation, only five inputs and five target outputs are selected. This is the result of various considerations. Given the desire to develop a realistic methodology, the input variables are first filtered by assessing their availability from measurement devices. Next, the relationships between the variables are analyzed through cross-correlation to determine the degree of influence of each input on the output.
Using this data, a training database is created. It is used to train two different types of surrogate models, one linear and one non-linear, respectively ARIMAX and LSTM. These are implemented to generate a 30-second forecast of the moments acting at the root of the blade. To do so, the algorithms are trained using different variables as exogenous inputs to assess the models' performance in different cases. Given the wide range of target features, for LSTM two different behaviors are identified and the blade edgewise and flapwise moments are taken as examples. The hyper-parameters are tuned on the blade edgewise moment and lead to overfitting when applied to the blade flapwise and out-of-plane moment.
The obtained results show that the RMSE in ARIMAX is up to seven times larger than the one obtained from the application of LSTM. Within the non-linear models, the one resulting in the lowest percentage error for the blade edgewise, pitching, and in-plane moment considers the wind reference speed, the wind speed time series, and the corresponding tip deflection as exogenous inputs. Very low RMSE errors are obtained for all variables. Furthermore, it is concluded that while it is possible to implement LSTM in real-life, this is not achievable for ARIMAX.