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.

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.

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.

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.

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.

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.

The electrification of ship power systems plays a center role in the mobility transition towards sustainable transport solutions. It allows the integration of various power sources, energy storage systems, and intermittent generation. The integration of an increasing number of components with distinct characteristics shapes the notion of a shipboard microgrid which benefits from a modular approach in its design to reduce costs and uncertainties. DC distribution facilitates the modular design by simplifying the control, and, combined with power electronics interfaces, increases the controllability of power flows in the system. To handle the increasing system complexity, this work proposes a distributed and predictive control approach, addressing the modular topology of future shipboard power systems and leveraging load power forecasting. Investigations show that a distributed, predictive energy management reaches a similar performance as a centralized implementation. For a modular shipboard power system, the proposed method decreases both fuel and degradation costs with increasing performance gains for longer prediction horizons.

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.

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.

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.

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.

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.

To ensure that future autonomous surface ships sail in the most sustainable way, it is crucial to optimize the per-formance of the Energy and Power Management (EPM) system. However, marine EPM systems are complex and often coordinate various distributed energy resources, energy storage systems, and power grids to ensure reliable and safe power delivery. Traditional control methods for marine EPM systems are limited by evaluating processes using simplified component models over a short time horizon, or relying on historical insights gained from earlier journeys, and are not the optimal approach for complex hybrid marine EPM systems. Advanced control strategies, such as Model Predictive Control (MPC), offer a promising control method that considers predicted future system responses over an extended time horizon to determine the best control input, making them an effective strategy for optimizing the performance of hybrid marine EPM systems. However, to learn the onboard energy profiles based on component behavior in a hybrid system from past experiences is not a trivial task, and one of the primary barriers to implementing MPC for marine EPM control. For this reason, in this work, we address the challenge of learning energy profiles for a marine EPM system by utilizing shallow and deep machine learning for total power demand forecasting. The forecast is an essential reference for an MPC-based controller and will enable this control strategy to provide reliable and safe power delivery for hybrid marine EPM systems. The proposed approach compares state-of-the-art machine learning models to identify the best-performing algorithm, considering accuracy and computational requirements. We illustrate the potential of the proposed approach by using real world operational data from a vessel with a hybrid marine EPM system. Results indicate that shallow models, trained on engineered features handcrafted with classical signal processing techniques, allow forecasting the total power demand up to a horizon of 5min with minimal loss in accuracy and a negligible computational burden.

Floating offshore wind turbines are perceived as a techno-economically attractive solution due to their huge potential, however, their Levelised Cost of Energy (LCoE) has the potential to be further reduced, making it more competitive with current technology. The Multidisciplinary Design, Analysis, and Optimisation (MDAO) method is considered a promising way to reduce LCoE. The substructure is expected to accounts for around 35% of the capital cost hence optimisation frameworks have been applied to this area in numerous studies. A difficulty with this method is creating a flexible and robust framework which can model all platform types (i.e., waterplane, ballast, and mooring stabilised configurations), which, simultaneously, remains computationally affordable. In this work, a concept selection method to rank the platform types based on their suitability for a given site is provided. The approach allows a reduction of the design space for an MDAO approach, creating substantial computational time savings. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a Multi-Criteria Decision Analysis (MCDA) method which is used in this work to rank the platform types in order of relevance for a given site. This technique allows a decision to be made when there are conflicting parameters such as cost and performance. The parameters are prescribed a weighting value by the user, representing the importance based on their specific point of view. In order to determine which platform is appropriate for a specific site, a number of criteria were set related to the site’s water depth, tidal range, soil condition, and wave height. This determines which platform is most suitable based on the physical parameters related to the site. The ranking can be found by combining the weighting of each parameter, the criteria related to the site, and the physical characteristics of each site. In order to support our proposed method, a case study considering the recent Scotwind lease is performed, showing that the derived best solutions are largely in agreement with the platform types considered by the developers.

Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR-assisted pitch control. This approach uses wind velocity measurements from a nacelle-mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs) and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC + FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely, in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR-assisted pitch control may allow for an up to 5% reduction in OpEx, increasing to up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR-assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR-assisted pitch control.

Floating Offshore Wind Turbines (FOWT) can harness the abundant wind resource in deep-water offshore conditions. However, they face challenges in harsh, unsheltered marine environments. The mean hydro- and aerodynamic loads coupled with fluctuating stochastic wind and wave loads contribute to varied failure mechanisms. Therefore, the serviceability, ultimate, and fatigue limit states are vital in ensuring the safety and reliability of FOWT. This paper investigates how specific loads and states drive the design of a spar-type support structure, utilising a computationally efficient frequency-domain model. This approach combines quasi-static aerodynamic and mooring models with a potential-theory-based radiation-diffraction solver. The serviceability criteria concern the platform and tower top displacements and accelerations. The ultimate and fatigue limit states are assessed for the tower base, the waterline section, and the mooring lines, including the effects of yielding under the bending moment and compressive axial load, column buckling, and tension-tension effects in the mooring lines. The full factorial design of experiments is employed to investigate the non-trivial relationships between the limit states and the various features of the support structure. The results demonstrate that the design of the spar platform above the waterline is mainly driven by fatigue, which results from significant dynamic tilt and increased stress concentration at the platform-tower intersection. On the other hand, the catenary mooring lines' design is mainly driven by the requirements of maximum offset (serviceability limit state) and fatigue.

The sustainability policy requires the Netherlands Ministry of Defense (MoD) to become largely independent of fossil fuels in the coming decades, while at the same time the geopolitical situation requires the Armed Forces to operate globally. Therefore, the Royal Netherlands Navy needs to reconsider its fuel strategy, to gradually change to energy carriers from renewable sources, and to redesign its power and propulsion system, both to improve efficiency and to reduce its emissions and signatures. The aim of this chapter is to provide a critical review of the challenges and opportunities of the transition to alternative energy carriers and energy systems and to provide a direction for the required research and development towards the future Navy fleet that can operate independently of fossil fuels and with minimal signature. First, the chapter reviews production, expected availability and projected development of cost of the various alternative fuels, with a specific focus on hydrogen, methanol, and renewable drop-in alternatives for naval distillate fuels, according to NATO F-76 spec- ifications. Subsequently, the chapter will discuss the impact of these three fuels on three differently sized navy vessels, that are currently considered for replacement: diving vessels, seagoing support vessels, and future surface combatants. For these three use cases, the chapter also discusses the potential power and propulsion system configurations and the opportunities to reduce signatures by alternative power supplies, such as fuel cells and batteries. Based on this analysis, the chapter will propose a fuel selection strategy for future navy vessels and will discuss the required research and development that is required to achieve the identified opportunities. The chapter closes with conclusions and recommendations on the route towards a future fleet that can operate globally independent of fossil fuels.