T. Dammann
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Wind farms experience reduced power production and elevated structural loading due to wake interactions. Wake-mixing control techniques, which dynamically excite upstream turbine wakes to accelerate recovery, have demonstrated promising improvements in downstream power production but at the expense of increased fatigue loading. Identifying the optimal control settings and quantifying the resulting load implications remain challenging because these methods require high-fidelity simulations that capture both the dynamic actuation and the resulting turbulence. Moreover, existing load surrogate models do not incorporate wake-mixing control, largely because conventional engineering wake models are unable to reproduce periodic wake excitation. This study presents two complementary advances to improve the design of wake-mixing strategies using a limited number of large eddy simulations (LES) and Gaussian process (GP) regression. First, we develop an efficient simulation-driven framework to identify optimal frequency and amplitude parameters for wake-mixing control, yielding a clear optimal power gain of 7.5 % near a Strouhal number of 0.25 and pitch amplitudes of around 4° for a two-turbine array. Second, we present a surrogate model capable of predicting fatigue loads for wake-mixing control. Using LES-derived rotor-plane inflow fields for aeroelastic simulations, we construct a load database that encompasses various combinations of wake overlap, turbine spacing, and wind farm control settings. The result is a load surrogate model based on GP regression trained on sector-averaged inflow quantities that accurately predicts damage equivalent loads, including the effect of increased excitation in the wake. This model enables the joint evaluation of power gains and load penalties at the wind farm level, supporting a more informed design of wake-mixing control strategies. Applying the load surrogate model to a two-turbine case study demonstrates the trade-off between additional power gain and increased structural loading of both turbines with the helix method.
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Wind farms experience reduced power production and elevated structural loading due to wake interactions. Wake-mixing control techniques, which dynamically excite upstream turbine wakes to accelerate recovery, have demonstrated promising improvements in downstream power production but at the expense of increased fatigue loading. Identifying the optimal control settings and quantifying the resulting load implications remain challenging because these methods require high-fidelity simulations that capture both the dynamic actuation and the resulting turbulence. Moreover, existing load surrogate models do not incorporate wake-mixing control, largely because conventional engineering wake models are unable to reproduce periodic wake excitation. This study presents two complementary advances to improve the design of wake-mixing strategies using a limited number of large eddy simulations (LES) and Gaussian process (GP) regression. First, we develop an efficient simulation-driven framework to identify optimal frequency and amplitude parameters for wake-mixing control, yielding a clear optimal power gain of 7.5 % near a Strouhal number of 0.25 and pitch amplitudes of around 4° for a two-turbine array. Second, we present a surrogate model capable of predicting fatigue loads for wake-mixing control. Using LES-derived rotor-plane inflow fields for aeroelastic simulations, we construct a load database that encompasses various combinations of wake overlap, turbine spacing, and wind farm control settings. The result is a load surrogate model based on GP regression trained on sector-averaged inflow quantities that accurately predicts damage equivalent loads, including the effect of increased excitation in the wake. This model enables the joint evaluation of power gains and load penalties at the wind farm level, supporting a more informed design of wake-mixing control strategies. Applying the load surrogate model to a two-turbine case study demonstrates the trade-off between additional power gain and increased structural loading of both turbines with the helix method.
Journal article
(2026)
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M. Baricchio, D.C. van der Hoek, T. Dammann, Pieter M.O. Gebraad, J. Iori, J.W. van Wingerden
Wind farm flow control mitigates wake effects by adjusting turbine settings to improve overall farm performance rather than the output of each turbine. Wake steering is an established wind farm flow control approach, while the helix method has recently emerged as a promising solution that enhances wake recovery by increasing mixing with the free-stream flow. This study quantifies the value of a combined strategy, in which each turbine can apply wake steering or the helix method. The analysis is performed considering different levels of uncertainty in wind direction, using engineering wake models that enable the simulation of these techniques on large-scale wind farms. A novel optimization algorithm, called multi-strategy serial-refine (MSR), is developed in this study, extending the state-of-the-art yaw-optimization method to include multiple control strategies and a generalized objective. A scaled version of an offshore wind farm in the Netherlands is selected as the case study, consisting of 69 IEA 22 MW turbines. The proposed combined strategy results in a greater increase in annual energy production than either individual strategy, leveraging the effectiveness of the helix method when multiple misaligned downstream turbines are present. This trend persists even under wind direction uncertainty. Due to the high sensitivity of wake steering to such uncertainty, the combined strategy benefits from the superior robustness of the helix method under these conditions.
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Wind farm flow control mitigates wake effects by adjusting turbine settings to improve overall farm performance rather than the output of each turbine. Wake steering is an established wind farm flow control approach, while the helix method has recently emerged as a promising solution that enhances wake recovery by increasing mixing with the free-stream flow. This study quantifies the value of a combined strategy, in which each turbine can apply wake steering or the helix method. The analysis is performed considering different levels of uncertainty in wind direction, using engineering wake models that enable the simulation of these techniques on large-scale wind farms. A novel optimization algorithm, called multi-strategy serial-refine (MSR), is developed in this study, extending the state-of-the-art yaw-optimization method to include multiple control strategies and a generalized objective. A scaled version of an offshore wind farm in the Netherlands is selected as the case study, consisting of 69 IEA 22 MW turbines. The proposed combined strategy results in a greater increase in annual energy production than either individual strategy, leveraging the effectiveness of the helix method when multiple misaligned downstream turbines are present. This trend persists even under wind direction uncertainty. Due to the high sensitivity of wake steering to such uncertainty, the combined strategy benefits from the superior robustness of the helix method under these conditions.
Engineering wake models are essential tools in wind farm design and operation. Their computational efficiency enables rapid layout optimization, energy calculation, and turbine control setpoint design. As wind farms grow in scale and density, wake interactions between turbines limit overall energy capture and increase structural loading. Wind Farm Control strategies have become critical for mitigating these effects. While wake steering, the intentional yaw misalignment of upstream turbines, has consistently demonstrated power gains, new dynamic control strategies are emerging. Among these, the Helix approach has shown particular promise in accelerating wake recovery and improving downstream power production. While engineering models for wake steering are well established, dedicated models for the Helix approach remain unavailable. This study presents a novel steady-state engineering model for predicting the velocity deficit and added turbulence of wind turbines operating under Helix actuation. The proposed model extends double-Gaussian velocity deficit and bell-curve– shaped turbulence intensity formulations to account for Helix-specific effects of actuation amplitude and ambient turbulence. Calibration and validation against high-fidelity large-eddy simulations demonstrate strong agreement across a wide range of operating conditions. The model accurately reproduces wake recovery trends, variations in the velocity-deficit profile, turbulence distributions, and the resulting downstream power availability. Finally, a case study on a large offshore wind farm illustrates that, under typical offshore atmospheric conditions, Helix control and wake steering yield individual power gains of up to 2.5% and 6.1%, respectively, while their combined application achieves total power gains of up to 7.1%.
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Engineering wake models are essential tools in wind farm design and operation. Their computational efficiency enables rapid layout optimization, energy calculation, and turbine control setpoint design. As wind farms grow in scale and density, wake interactions between turbines limit overall energy capture and increase structural loading. Wind Farm Control strategies have become critical for mitigating these effects. While wake steering, the intentional yaw misalignment of upstream turbines, has consistently demonstrated power gains, new dynamic control strategies are emerging. Among these, the Helix approach has shown particular promise in accelerating wake recovery and improving downstream power production. While engineering models for wake steering are well established, dedicated models for the Helix approach remain unavailable. This study presents a novel steady-state engineering model for predicting the velocity deficit and added turbulence of wind turbines operating under Helix actuation. The proposed model extends double-Gaussian velocity deficit and bell-curve– shaped turbulence intensity formulations to account for Helix-specific effects of actuation amplitude and ambient turbulence. Calibration and validation against high-fidelity large-eddy simulations demonstrate strong agreement across a wide range of operating conditions. The model accurately reproduces wake recovery trends, variations in the velocity-deficit profile, turbulence distributions, and the resulting downstream power availability. Finally, a case study on a large offshore wind farm illustrates that, under typical offshore atmospheric conditions, Helix control and wake steering yield individual power gains of up to 2.5% and 6.1%, respectively, while their combined application achieves total power gains of up to 7.1%.
Benchmarking numerical models is essential for validating their accuracy and ensuring consistency across simulation platforms. This study presents a comparative benchmark analysis of two widely used Large Eddy Simulation (LES) codes, AMR-WIND and NREL SOWFA-6, focusing on wind turbine rotor performance, wake dynamics, and atmospheric boundary layer (ABL) representation. The evaluation includes an actuator line model (ALM)-based uniform inflow wind turbine simulation and ABL precursors under neutral and unstable conditions. The uniform inflow wake analysis examined differences in wind turbine induction and wake development between the two codes. Additionally, neutral and unstable atmospheric boundary layer precursors were generated for an offshore environment and compared. Results indicate a difference in wake breakdown location between the codes (one contributing factor was the difference in numerical schemes used for the advection terms.) The number of actuator points required for smooth velocity distribution across the rotor was higher for SOWFA-6 than AMR-WIND. In ABL precursors, time-averaged flow fields showed strong agreement, though minor discrepancies in turbulence were observed, particularly in unstable conditions, affecting coherence analysis. The energy distribution across wavenumbers showed a good match between the codes, with slight discrepancies observed in the large and small wavenumber regions. The cutoff wavenumber was found to be similar for both codes. Lateral and vertical coherence at small and large separations were in close agreement for the neutral ABL. However, in the unstable ABL, notable differences in coherence were observed between the codes for separations greater than 40 m.
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Benchmarking numerical models is essential for validating their accuracy and ensuring consistency across simulation platforms. This study presents a comparative benchmark analysis of two widely used Large Eddy Simulation (LES) codes, AMR-WIND and NREL SOWFA-6, focusing on wind turbine rotor performance, wake dynamics, and atmospheric boundary layer (ABL) representation. The evaluation includes an actuator line model (ALM)-based uniform inflow wind turbine simulation and ABL precursors under neutral and unstable conditions. The uniform inflow wake analysis examined differences in wind turbine induction and wake development between the two codes. Additionally, neutral and unstable atmospheric boundary layer precursors were generated for an offshore environment and compared. Results indicate a difference in wake breakdown location between the codes (one contributing factor was the difference in numerical schemes used for the advection terms.) The number of actuator points required for smooth velocity distribution across the rotor was higher for SOWFA-6 than AMR-WIND. In ABL precursors, time-averaged flow fields showed strong agreement, though minor discrepancies in turbulence were observed, particularly in unstable conditions, affecting coherence analysis. The energy distribution across wavenumbers showed a good match between the codes, with slight discrepancies observed in the large and small wavenumber regions. The cutoff wavenumber was found to be similar for both codes. Lateral and vertical coherence at small and large separations were in close agreement for the neutral ABL. However, in the unstable ABL, notable differences in coherence were observed between the codes for separations greater than 40 m.
Enhanced Wind Farm Performance via Active Wake Control
A Steady-State Approach
Denser turbine spacing in wind farms leads to increased wake interactions, causing power losses when each turbine operates under its own greedy control scheme. To mitigate these effects, research is exploring strategies that consider the entire wind farm rather than singular turbines. The so-called helix approach has recently gotten significant attention from the research community. It aims to reduce wake losses through periodic individual pitch control. Wake steering on the other hand uses yaw actuation to laterally deflect the wake away from downstream turbines. In this paper, we adapt and validate a steady-state surrogate model to compute the time-averaged velocity field behind a wind turbine operating with the helix approach. The model is tuned using data from Large Eddy Simulations. We compare the helix model to wake steering and baseline operation in a wind farm case study, demonstrating that the helix approach offers promising benefits under specific wind conditions.
...
Denser turbine spacing in wind farms leads to increased wake interactions, causing power losses when each turbine operates under its own greedy control scheme. To mitigate these effects, research is exploring strategies that consider the entire wind farm rather than singular turbines. The so-called helix approach has recently gotten significant attention from the research community. It aims to reduce wake losses through periodic individual pitch control. Wake steering on the other hand uses yaw actuation to laterally deflect the wake away from downstream turbines. In this paper, we adapt and validate a steady-state surrogate model to compute the time-averaged velocity field behind a wind turbine operating with the helix approach. The model is tuned using data from Large Eddy Simulations. We compare the helix model to wake steering and baseline operation in a wind farm case study, demonstrating that the helix approach offers promising benefits under specific wind conditions.