The push to attain commercialization of the floating offshore wind industry and subsequently achieving net-zero carbon emission by the year 2050 requires the utilization of cutting-edge design and analyses techniques. Geometric design parameterization and optimization is an effective technique that can be employed in modelling and optimizing a Floating Offshore Wind Turbine (FOWT) substructure. It is an essential framework with the capability of innovative concept generation of platform types in the FEED design phase. This study addresses the conceptual design shape generation, multidisciplinary design analysis and optimization (MDAO) of spar variants FOWT substructure developed from the standard NREL OC3 spar. The methodology involves the use of non-uniform rational basis spline (NURBS) parameterization technique to generate design variants with the flexibility of varying the control points to facilitate varying geometric shapes due to the local propagation property of the NURBS curve. Design variables passed through the NURBS curves control points generates a robust and rich design space and the potential flow hydrodynamic analysis tool in the DNV SESAM suite is used to estimate the hydrodynamic response. The design and analysis phases are explored and exploited for optimal design solution based on specified objectives and constraints with the use of state-of-the-art derivative-free optimizers. The optimal designs were evaluated for three sets of FOWT static pitch angle constraints (5, 7 and 10) degrees, a positive ballast constraint for stability and a constraint on nacelle acceleration root mean square (RMS) value below 30 % of the gravitational acceleration. The single objective function considered in the study is to ensure a minimum mass of the steel material utilized in the design, which invariably leads to a reduction in cost of the substructure material used in fabrication. Achieving this single objective results in an altered geometric shape variants from the baseline OC3 spar substructure for all the three cases evaluated. Verification of the nacelle acceleration response in time domain was further evaluated for the three optimal design cases selected and compared with recommended standards which is below 0.3 g. Although, the nacelle acceleration for the optimal variants is more conservative in time domain assessment than the frequency domain assessment, the values are still below the recommended 0.3 g from standards. Also, the masses of selected optimal design for each constraint were compared to the standard OC3 case study. An observation made in this study is that as the static pitch angle of the FOWT system gets larger, the lower the mass of the optimal substructure and inherently the capital expenditure of the substructure. Finally, the selected optimized platforms were analysed with a non-linear, time domain approach to confirm the level of accuracy of the key response parameters obtained with the frequency-based approach.

The maritime sector aims to achieve short and medium-term sustainability targets through the conversion of Internal Combustion Engines to methanol operation. For small to medium sized engines, Port Fuel Injection (PFI) is the most viable injection method to achieve this conversion. However, the knowledge of the behaviour of methanol in combustion engines, particularly its spray characteristics under PFI conditions, is limited. To better understand liquid methanol sprays, this paper studies the injection of methanol in marine PFI conditions through Computational Fluid Dynamics (CFD) modelling. The CFD models use the Lagrangian-Eulerian (LE) coupling method within the Reynolds Averaged Navier Stokes (RANS) turbulence framework. Numerical results were validated using dedicated methanol experiments from the literature for both high and low injection pressures. Subsequently, this predictive CFD framework was used in a number of different injection pressures with scaled injection quantities that represent marine applications. Moreover, we demonstrated that high injection pressure improves atomisation and, thus, evaporation prior to wall impingement. This work strongly contributes to our understanding of marine PFI methanol engines by modelling fuel quantities relevant for ship applications. Our approach can be implemented in full engine simulations to solve evaporation challenges often found in small-bore methanol marine engines.

A key factor towards zero-emission shipping is the adoption of electric propulsion with hybrid power sources. The heterogeneous power sources of modern electric vessels require optimal energy management systems, as conventional rule-based control in hybrid energy systems may result in suboptimal solutions with limited flexibility. Advanced optimal control strategies offer a promising avenue to address this issue. This paper presents a novel control strategy based on the Equivalent Consumption Minimization Strategy for a dual-fuel full-electric vessel operating with diesel engines and hydrogen fuel cells taking into account both fuel cost and NOx emissions. The effectiveness of the developed controllers is evaluated against a benchmark derived from state-of-the-art strategies in a simulation study using real-world data. The results highlight the controller's performance, as well as the operator's choice by selection of weights for the objectives. The proposed control strategy achieves nearly 2 % fuel savings compared to a single-objective rule-based controller. It also exploits the potential for up to 45 % reductions in NOx emissions. When both objectives are combined, the controller still delivers over 0.5 % fuel savings while reducing NOx emissions by nearly 15 %. If a financial cost is assigned to emissions, the total operational cost savings increase to more than 4 %.

The dynamic behaviour of floating offshore wind turbines (FOWTs) involves complex interactions of multivariate loads from wind, waves, and currents, which result in complex motion characteristics. Although methods for analysing global motion responses are well-established, the time- and location-dependent kinematics remain underexplored. This paper investigates the instantaneous centre of rotation (ICR), a point of zero velocity at a time instance of general plane motion. Understanding and strategically positioning the ICR can reduce the dynamic motion in critical structural locations, enhancing the performance and structural robustness of FOWTs. The paper presents a method for computing the ICR using time-domain simulation results and proposes a statistical analysis approach suitable for design studies. Building on prior research, it examines the sensitivity of the ICR to external loading and design features, providing insights into how these factors influence motion response and how the motion response influences the statistics of the ICR, structural loads, and other performance metrics of interest. The study explores two FOWT configurations, a spar and a semisubmersible, identifying design variables that most effectively control the ICR statistics and identifying the ICR statistics most correlated with the responses of interest. Finally, through two case studies, we demonstrate how to apply these new insights in a practical design scenario. By adjusting the design variables most correlated with the ICR (fairlead vertical position and centre of mass for the spar and mooring line length and offset column diameter for the semisubmersible), we successfully modified the designs of the floating support structures to reduce the loads in the mooring lines, tower base, and blade roots, improving the ultimate strength and fatigue characteristics compared to the original designs.

In the scope of the energy transition, the maritime industry, still heavily relying on fossil fuels, is facing expectations to reduce its carbon output. Electrified shipboard power systems (SPSs) equipped with hydrogen fuel cells (FCs) and energy storage systems (ESSs) are a promising solution for the shift to zero-emission shipping. A remaining challenge is the efficient coordination of multiple parallel power generation and storage modules. This article proposes a modular approach to the power system control to offer a plug-and-play capability for multiple FCs and ESSs, facilitating a topology reconfiguration. Virtual impedance-based droop is implemented to achieve power sharing and load frequency decoupling in a decentralised architecture. An additional low-bandwidth communication is leveraged to enable parameter adaptation after a topology reconfiguration. The methodology is tested numerically with a short-sea cargo vessel serving as a case study. The local controllers are tuned to achieve load frequency decoupling between FCs and batteries matching the specified time constant. For a maneuvering power profile, the average FC power gradient could be decreased by 36%, limiting their degradation caused by dynamic operation, while increasing the depth-of-discharge of the batteries. The simulations further show that an adaptation of control parameters after a component fault can be used to maintain the system’s voltage dynamics. The voltage drop caused by a load step in a reconfigured system that disconnected one of two ESS could be reduced by 37.5% by control parameter adaptation.

To reduce emissions, methanol is a favorable carbon-neutrally-producible alternative fuel, which can substitute gasoline in direct-injection spark-ignition (DISI) engines. Robust DISI engine operation relies on a consistent air-fuel mixture. To understand the physical processes that characterize the mixture formation, predictive computational fluid dynamics (CFD) simulations are used to improve the understanding, operation, and emissions of these engines. However, using an alternative fuel such as methanol often poses challenges to the CFD simulations’ validity due to the alteration of the fuel properties. This study presents the validation of a CFD modeling approach that can be applied to the predictive modeling of DISI methanol engines. Our methodology uses Lagrangian-Eulerian methods to model the methanol eight-hole counter-bore style Spray M injector from the Engine Combustion Network (ECN). We used the Spray M1 condition, which represents a late-injection spray under a high ambient pressure and temperature environment. For the present study, we employed both a Reynolds Averaged Navier Stokes (RANS) and a Large Eddy Simulation (LES) turbulence approach in CONVERGE-CFD. To validate our models, we used the projected liquid volume (PLV) maps generated by the tomographic liquid volume fraction (LVF) based on methanol. Subsequently, we tuned our models based on the corresponding numerical predictions of the liquid penetration and LVF distributions. The results demonstrated that both the RANS and LES models could replicate the spray morphology and liquid length. While the RANS model was unable to fully capture the complex phenomena of spray collapse and sweeping in the methanol multi-hole spray, the LES model effectively reproduced these behaviors without excessive tuning effort.

The electrification of shipboard power systems carries an increasing variety in power sources, energy storage systems, and power converters. DC distribution is gaining relevance due to efficiency increase, space savings, and high controllability. The dominant primary control method voltage droop control, which offers easily implementable and scalable power sharing and voltage stabilization. However, compared to terrestrial microgrids, shipboard power systems have low line impedances and highly fluctuating loads. Most primary loads are power-controlled, introducing a non-linearity that leads to a weak damping and unstable operation points. To handle this non-linearity, this study proposes an energy-based control approach as an alternative to the voltage-based scheme. The controller is further extended by integral feedback loop to achieve fast voltage restoration. A low-bandwidth communication is leveraged for an adaptive power sharing control that facilitates an efficient allocation of the load among parallel units under varying conditions. The proposed control structure is deployed on an I/O board embedded in a hardware-in-the-loop testbed. It is shown that the energy-based controller operates stably and achieves a reduced voltage deviation from the nominal voltage in various load conditions compared to the conventional voltage-based method.

In contemporary industrial applications, predictive models have been pivotal in bolstering production efficiency, product quality, scalability, and cost-effectiveness while promoting sustainability. These predictive models can be constructed solely based on domain-specific knowledge, exclusively on observational data, or by amalgamating both approaches. They are commonly referred to as Full-, Zero-, or Partial-knowledge-based predictive models, respectively. Full-knowledge based models are highly explainable, point-wise accurate, data efficient, computationally demanding in the prediction phase to achieve high accuracy. Zero-knowledge based models are poorly explainable, data hungry, highly accurate on average, computationally demanding in the construction phase but inexpensive the prediction phase. Partial-knowledge based models focus on taking the best of the two worlds. To maintain a focused scope and avoid redundancy with existing literature, our review will primarily delve into the key industrial applications within a narrow context, encompassing sectors such as extraction, chemical processes, manufacturing, transportation, energy, and construction. Our approach entails several steps. Initially, we conducted a meta-review to pinpoint gaps in previously published surveys on Full-, Zero-, and Partial-knowledge-based predictive models for industrial applications. Subsequently, we present a formal analysis of the subject matter, supplemented with illustrative examples to offer valuable insights. The core of our work comprises a review of existing research categorized by specific industrial applications. Finally, we outline the unresolved challenges and future prospects in this burgeoning field of research. We contend that our work serves as a valuable resource, catering to the needs of both young researchers seeking a solid foundation for their studies, industrial practitioners aiming to grasp core concepts and applications, and senior researchers seeking potential real-world applications for their findings.

Methanol is a promising alternative fuel, which can assist in reducing emissions in heavy-duty (HD) dual-fuel (DF) compression ignition (CI) engines. In medium and large bore marine engines, DF operation is achieved through either direct injection (DI) or port fuel injection (PFI) of methanol with diesel acting as a DI pilot fuel for ignition. However, the injection of methanol presents a significant challenge due to its high latent heat of vaporization and decreased lower heating value (LHV) compared to diesel. Therefore, for the same energy content operation, methanol requires around eight times the amount of heat to evaporate completely in comparison to diesel, which results in lower in-cylinder temperatures. This charge cooling effect leads to a strong negative temperature gradient influencing ignition and flame propagation. This paper aims to quantify the cooling effect of methanol in a heavy-duty dual-fuel direct injection compression ignition (DICI) engine environment. The presented methodology uses computational fluid dynamics (CFD) simulations to model methanol sprays with validation originating from the engine combustion network (ECN) Spray D experimental data. The CFD models operate within the Lagrangian–Eulerian framework in CONVERGE-CFD using the Reynolds Averaged Navier Stokes (RANS) turbulence modeling. Compared to diesel, injecting methanol with the same energy content exhibited up to 100 K more decreased temperature within the mixture. Consequently, this cooled mixture may pose challenges to combustion stability due to the intense temperature gradients. Nonetheless, lower mixture temperature decreases NOx emissions, which can prove beneficial for high methanol energy fractions in dual-fuel DICI engines.

Renewably produced methanol is a promising fuel for internal combustion engines in long-range transportation thanks to its scalability, liquid storage, and favorable combustion properties. However, the distinction between different injection and ignition strategies for methanol engines and the resulting combustion mechanisms has not been consistently defined. Moreover, diffusion combustion strategies are favored over premixed strategies in large engines because of higher methanol energy fractions, disregarding the advantages of premixed approaches, such as reduced nitrogen oxide emissions and retrofitting opportunities. To address ambiguity in terminology, this paper proposes a classification framework for injection and ignition strategies and applies it to methanol-fueled internal combustion engines. Subsequently, this review focuses on experimental studies of methanol-fueled heavy-duty engines, which are crucial for transitioning to renewable and sustainable energy in long-range transportation. This research summarizes the impact of the reviewed injection and ignition strategies on combustion characteristics, engine performance and emissions to identify key trends. Furthermore, this review highlights how specific design and operating parameters influence premixed dual-fuel combustion, offering insights into optimizing performance and emissions. While mono-fuel and premixed dual-fuel strategies with methanol can significantly promote methanol use in heavy-duty engines and reduce harmful emissions like nitrogen oxides, a rise in unburned hydrocarbon emissions may also be expected, necessitating further research in this area. Additionally, methanol injection location in premixed dual-fuel schemes affects its cooling effect, influencing volumetric and thermal efficiency. Overall, this study deepens our understanding of methanol's impact on heavy-duty engine performance, highlighting critical challenges to be addressed for advancing sustainable transportation.

Hybrid power systems are increasingly adopted onboard. Lithium-ion batteries now serve as a viable energy storage solution that enhances fuel efficiency and reduces the operating hours of main power units, thereby reducing operational expenses. However, integrating batteries onboard requires decision-making that accounts for diverse scenarios, including battery chemistry, variations in vessel operational profiles, and fluctuating fuel prices. To address these challenges, this study investigates whether battery sizing and scheduling of the power and energy management system require a scenario-based stochastic decision framework. Specifically, it examines how energy storage requirements are influenced by varying load profiles, whether the optimal battery size and power management strategy are affected by fuel price fluctuations, and how robust the overall strategy remains under operational uncertainties. A deterministic equivalent of a two-stage stochastic decision framework is introduced to incorporate these uncertainties, offering insights into the required battery technology, capacity, and correlated behavior of onboard energy management. Multiple scenarios are applied to a trailing suction hopper dredger, analyzing three load profiles with distinct variations in power demand. With reserve power constraints enforced, the optimal battery capacity remains fixed. However, when these constraints are relaxed, the optimal battery size becomes more sensitive to fuel price changes. In addition, the results showcase reduction in diesel engine operating hours—thereby lowering both fuel consumption and maintenance costs, demonstrating that these operational benefits depend not only on the battery's size but also on its available throughput, which allows for deeper cycling.

Optimizing the hull form is crucial for enhancing vessel performance, requiring a careful exploration of design variations once an initial concept is chosen. To address this, a well-defined parameterization and search space must be established, followed by a numerical optimization process. This phase involves balancing three key requirements: improving performance indicators like resistance (and consequently emissions), ensuring physical plausibility (e.g., designs that meet stability standards), and maintaining computational efficiency. While existing methods partially address these challenges using surrogate models to optimize performance, conducting a-posteriori checks for physical plausibility, and reducing the search space a-priori, they fall short of fully integrating these aspects. In this work, we introduce a two-fold approach to address these challenges. First, we integrate a key physical plausibility criterion directly into the optimization loop: the International Maritime Organization (IMO) Intact Stability Code. For the first time, we use a surrogate model to include this constraint within the numerical optimization process. This enables the generation of designs that inherently meet stability requirements. Second, we reduce the computational demands by applying data-driven methods to limit the search space in a problem-specific manner. This targeted reduction reduces the computational load without compromising the overall optimization performance. We test our proposal by optimizing the KCS hull form. Our results demonstrate two main achievements. First, we find that current optimization pipelines fail to meet the IMO stability guidelines, whereas our method achieves compliance by design. Second, our approach reduces the computational burden by 30 % without sacrificing performance, representing a significant leap forward in practical hull design optimization.

Relying on a traffic simulator is often necessary when multiple traffic conditions need to be replicated, either to predict specific quantities of particular interest or to assess more general network properties, such as efficiency or resilience, under different scenarios. While potentially delivering a high level of fidelity, producing such a simulation may come at a prohibitive computational cost when it is applied in a real context, making this approach unsuitable for real-time applications in most cases. In this regard, the goal of the present work is twofold. Firstly, we aim to surrogate a traffic simulator with a data-driven approach in order to produce real-time traffic predictions that are also sufficiently accurate and effective. Secondly, we want to determine the minimum amount of data, i.e., the smallest number of sensors deployed on the network, that still allow to obtain predictions within a predefined bound. The effectiveness of the approach is evaluated on the full-scale urban network of Rapallo, Italy, in which we employ the AIMSUN NEXT simulator targeting the morning peak hours, i.e. between 7:00 a.m. and 9:00 a.m. In the paper, multiple state-of-the art ML algorithms are tested to assess their effectiveness as surrogate models under the considered problem.

Methanol has emerged as a cost-effective and scalable alternative fuel for the maritime sector. However, the use of methanol in marine engines is limited by the unknown characteristics of methanol sprays when introduced through retrofitted port fuel injection (PFI) systems. The present study investigates the characteristics of methanol sprays under relevant conditions for marine engines, such as low injection pressure PFI. The primary objective of this research is to advance knowledge into key spray characteristics, including spray penetration, droplet size, atomization quality, and evaporation. The proposed methodology evaluates the efficacy of state-of-the-art computational fluid dynamics (CFD) models in simulating PFI marine engine spray conditions. Moreover, the study compares the performance of the Kelvin-Helmholtz (KH-RT) and Taylor Analogy Breakup (TAB) droplet breakup models under low injection pressure conditions. The results demonstrated that the KH-RT model does not predict any droplet breakup occurrence suggesting that the TAB model is more suitable for the given conditions. Furthermore, the liquid penetration of the spray was observed to align with the outcomes reported in previous experimental literature on methanol sprays. Nevertheless, the droplet sizes for low pressure injectors appear relatively large, indicating poor spray atomization, which impedes rapid evaporation and increases the risk of wall wetting in the inlet manifold and combustion chamber.

The presence of contact and backlash often leads to complex and rich dynamic responses in mechanical structures. This typically occurs in systems such as jointed structures and geared mechanisms, where the inherent complexity leads to responses which are difficult to predict. In particular, identifying models capable of performing reliable predictions is extremely challenging, both for the definition of the equations of motion and for the characterisation of their parameters. In this chapter, we present a systematic approach based on the restoring force surface method for analysing and identifying the localised piecewise nonlinear characteristics in multi-degree-of-freedom systems. Our approach builds on the knowledge of the underlying linear system and outlines a procedure to identify equivalent nonlinear characteristics via a graphic representation of the piecewise restoring forces. The approach is validated against experimental measurements of a test rig representative of a two-degree-of-freedom system with a localised nonsmooth characteristic. The reconstruction of the nonsmooth restoring force, identification of the piecewise characteristics, and the validation of the model against experimental time histories are performed following our systematic procedure, proving the flexibility of this practical approach.

Floating offshore wind turbines (FOWTs) are still in the pre-commercial stage and, although different concepts of FOWTs are being developed, cost is a main barrier to commercializing the FOWT system. This article aims to use a shape parameterization technique within a multidisciplinary design analysis and optimization framework to alter the shape of the FOWT platform with the objective of reducing cost. This cost reduction is then implemented in 30 MW and 60 MW floating offshore wind farms (FOWFs) designed based on the static pitch angle constraints (5 degrees, 7 degrees and 10 degrees) used within the optimization framework to estimate the reduction in the levelized cost of energy (LCOE) in comparison to a FOWT platform without any shape alteration–OC3 spar platform design. Key findings in this work show that an optimal shape alteration of the platform design that satisfies the design requirements, objectives and constraints set within the optimization framework contributes to significantly reducing the CAPEX cost and the LCOE in the floating wind farms considered. This is due to the reduction in the required platform mass for hydrostatic stability when the static pitch angle is increased. The FOWF designed with a 10 degree static pitch angle constraint provided the lowest LCOE value, while the FOWF designed with a 5 degree static pitch angle constraint provided the largest LCOE value, barring the FOWT designed with the OC3 dimension, which is considered to have no inclination.

Due to increasing environmental concerns and global energy demand, the development of Floating Offshore Wind Turbines (FOWTs) is on the rise. FOWTs offer a promising solution to expand wind farm deployment into deeper waters with abundant wind resources. However, their harsh operating conditions and lower maturity level compared to fixed structures pose significant engineering challenges, notably in the design phase. A critical challenge is the time-consuming hydromechanics analysis traditionally done using computationally intensive Computational Fluid Dynamics (CFD) models. In this study, we introduce Artificial Intelligence-based surrogate models using state-of-the-art Machine Learning algorithms. These surrogate models achieve CFD-level accuracy (within 3% difference) while dramatically reducing computational requirements from minutes to milliseconds. Specifically, we build a surrogate model for characterizing the hydrodynamic response of a floating spar-type offshore wind turbine (including added mass, radiation damping matrices, and hydrodynamic excitation) using computationally efficient shallow Machine Learning models, optimizing the trade-off between computational efficiency and accuracy, based on data generated by a cutting-edge potential-flow code.

Meta-heuristic optimisation algorithms are high-level procedures designed to discover near-optimal solutions to optimisation problems. These strategies can efficiently explore the design space of the problems; therefore, they perform well even when incomplete and scarce information is available. Such characteristics make them the ideal approach for solving nonlinear parameter identification problems from experimental data. Nonetheless, selecting the meta-heuristic optimisation algorithm remains a challenging task that can dramatically affect the required time, accuracy, and computational burden to solve such identification problems. To this end, we propose investigating how different meta-heuristic optimisation algorithms can influence the identification process of nonlinear parameters in mechanical systems. Two mature meta-heuristic optimisation methods, i.e. particle swarm optimisation (PSO) method and genetic algorithm (GA), are used to identify the nonlinear parameters of an experimental two-degrees-of-freedom system with cubic stiffness. These naturally inspired algorithms are based on the definition of an initial population: this advantageously increases the chances of identifying the global minimum of the optimisation problem as the design space is searched simultaneously in multiple locations. The results show that the PSO method drastically increases the accuracy and robustness of the solution, but it requires a quite expensive computational burden. On the contrary, the GA requires similar computational effort but does not provide accurate solutions.

Optimal energy management is still a challenge in full-electric vessels. New degrees of flexibility in the energy management resulting from the load sharing between multiple, heterogenous power sources lead to a suboptimal solution using rule-based control. Therefore, advanced control strategies present a solution to the challenge of finding the optimal control input for a nonlinear multi-objective power and energy problem in sufficient time. As additional benefit, advanced control allows to incorporate multiple objectives in the optimization such as minimization of several emissions, operational costs, and component degradation. Equivalent Consumption Minimization Strategy (ECMS) is a strategy for instantaneous optimization, which is promising for applications in vessels with a high degree of uncertainty in the load profile. It incorporates multiple objectives by assigning equivalent cost factors in the cost function, allowing a flexible expansion of the control problem. In this paper, we present a novel ECMS-based control strategy for a full-electric vessel with the ability to react flexibly to changing mission conditions. First, we define the objectives for the control problem, in this study \ce{CO2} production, hazardous emission production, fuel consumption, energy cost, and the degradation of the battery.
Second, we develop a pareto-front approach for a-posteriori definition of the equivalent cost factors. To showcase energy consumption reduction, we use a benchmark control based on state-of-the-art control strategies. A full-electric case study vessel with high uncertainty in the load profile is chosen to evaluate the proposed controller. Several different load profiles are generated and tested to evaluate the performance of the ECMS controller in dealing with different types of loads. The results will demonstrate the effectiveness of the proposed novel control strategy in reducing energy consumption while minimizing other hazardous emission outputs and preserving the health of the battery.