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.@en

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.

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Transshipment is a key component of modern-day shipping logistics. Container supply chains rely on tran-shipment hubs to access remote locations. With globalisation driving growth in container trade, maritime congestion is rising at container terminals in ports worldwide. This is expected to worsen as demand con-tinues to grow. This research explores novel maritime equipment designs that can contribute to solving problems in the trans-shipment chain. One such idea is that of the Amphibious Automated Guided Vehicle, an innovative concept that travels on both land and sea. Envisioned as a tool to minimise the rehandling of containers, the Amphibious AGV forms the heart of the new changes that this research proposes for the fu-ture of trans-shipment. Complementing swifter trans-shipment, the research also proposes complementary design concepts such as floating terminals to add more flexibility for container ships and Amphibious AGVs applied to exchange containers offshore. To validate these ideas, an agent-based modelling methodology was used and replicated in the environment of the Hong Kong- Pearl River Delta. This work, therefore, opens up an intriguing future scope for maritime transshipment that is both sustainable and adaptable while also discussing limitations and concerns that need to be carefully considered.@en

This study introduces a novel framework of metocean prediction and ship performance models that integrate multiple layers of modeling to evaluate the environmental impact of ship emissions. It enables scenario simulations that assess a ship's performance, estimates pollutant emissions, and simulate the fate of these pollutants in the atmosphere. The study analyzes the fate of NOx, SO₂, and PM10 pollutants in the atmosphere using spatially distributed concentration maps. It provides a comprehensive approach to assessing the environmental effects of ships and their emissions and contributes to the field of environmental impact assessment. Case studies are presented to demonstrate the framework's functionalities, evaluating the interrelationships between adverse meteo-marine conditions, pollutant emissions, and resulting atmospheric diffusion characteristics.

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The path to zero-emission shipping is deeply connected to full-electric vessels. One major challenge to enable this technology for broader application is the design of optimal energy management (EM). The flexibility of operating load sharing in hybrid energy systems could lead to suboptimal solutions using rule-based control. Advanced control strategies can be used to find optimal solutions for the EM problem. In addition, the use of advanced control allows for the incorporation of multiple objectives. An important compromise is the decision between minimizing cost and emissions. A promising approach for EM is the Equivalent Consumption Minimization Strategy (ECMS), which allows for instantaneous optimization of the problem and is suitable for dealing with fast system dynamics. The strategy assigns equivalent factors in the objective function, leading to an easily expandable multi-objective control approach.This paper presents a novel ECMS-based control strategy for health-aware EM of a full-electric vessel, incorporating diesel internal combustion engines, fuel cells, and batteries with flexible changing operation conditions. To this aim, firstly, we introduce our innovative formulation of the multi-objective problem, considering fuel and electricity expenditures and CO₂ and NO_x emissions, alongside the degradation of batteries and fuel cells. Subsequently, we determine the equivalent factors by employing a Pareto Front approach. Lastly, our developed controllers are assessed against a benchmark derived from state-of-the-art strategies. A case study of a full-electric vessel showcase the potential of our proposed solution. The results demonstrate the control's effectiveness in optimizing the operation considering a variety of objectives, such as fuel consumption or emission production, under variable operational conditions.

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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.@en

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.

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Shape optimization of vessel hulls and airfoils is crucial for achieving optimal performance and minimizing environmental impact. Typically, these designs are adaptations of existing ones, not fully optimized for specific Key Performance Indicators (KPIs) such as drag or lift, and their optimization often relies on a mix of human experience and numerical approaches. The current state-of-the-art approach leverages Computational Fluid Dynamics (CFD) Data-Driven Surrogate (DDS) models in a four-step process. First, a shape design space is created through parametrization, involving varying levels of human input. Accurate KPI estimation using CFD is computationally intensive, preventing direct optimization. Thus, in the second step, representative shapes are selected from the design space, and evaluated for their KPIs using CFD. Next, a DDS model is constructed from the generated data, which, although costly to develop, allows for efficient KPI prediction. This model is then integrated into an optimization loop to identify optimal geometries on the Pareto front. Finally, these results are validated through CFD to ensure physical plausibility. This review sets focuses on recent advances in DDS models for shape optimization of hulls and airfoils since 2015, an area not thoroughly covered in previous surveys. We systematically examine the four-step optimization process in recent studies, highlighting the evolution and deeper integration of DDS models with CFD. Additionally, we critically assess unresolved issues and gaps in current methodologies, exploring future research directions such as the application of machine learning for shape optimization. These elements highlight the novelty of our work by synthesizing recent technological advances and proposing pathways for future developments, bridging the gap between traditional methods and future possibilities in shape optimization, with implications for both academic research and industrial practice.@en

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.

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Optimizing vessel hull resistance is pivotal for enhancing maritime performance and minimizing environmental impacts. Traditional methods combine expert intuition with Data-Driven Models (DDMs), relying on parametrization to predict and optimize hull geometries using Experimental Fluid Dynamics (EFD) or Computational Fluid Dynamics (CFD) data. However, these conventional approaches are hampered by several limitations: they require significant human input, are computationally intensive and costly, and lack flexibility in adapting to new families of geometries or parameters beyond predefined ranges. Addressing these challenges, our research introduces a novel method that significantly reduces the need for human intervention, computational resources, and costs, while also improving the model's adaptability. By proposing a new a parametrization technique that accurately encompasses the Delft Systematic Yacht Hull Series (DSYHS), we demonstrate that DDMs can be effectively trained directly on EFD datasets. This eliminates the dependency on extensive CFD simulations or the generation of new EFD data tailored to a specific investigation. Our approach matches the performance of leading-edge CFD models, even in extrapolating conditions, with physical plausibility and minimal human oversight. The validation of our method under various and increasingly complex extrapolating scenarios, employing statistical analyses on the DSYHS EFD dataset and comparisons with state-of-the-art CFD models, underscores the effectiveness of our proposal. Furthermore, we demonstrated that our model can successfully optimize hull resistance when navigating geometric parameters outside the confines of the DSYHS validating our results through leading-edge CFD simulations. This work addresses the limitations of existing methodologies by offering a novel approach more accurate, efficient, cost-effective, flexible, automated, and robust to extrapolation for hull resistance optimization.

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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.

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The global shipping industry is at a crucial juncture, facing an urgent need to reduce greenhouse gas emissions in the short to medium term to mitigate climate change. A shift towards alternative fuels is imminent, necessitated by the limitations in current fuel cell and battery technology in terms of power density. Addressing this, navies worldwide are not only exploring the use of alternative fuels to diminish environmental impact but also seeking solutions to reduce emissions signatures and decrease reliance on fossil fuels. In this paper, we investigate the use of hybrid turbocharging to improve the dynamic performance of alternatively fueled combustion engines. We extended an existing and validated Mean Value First Principle (MVFP) engine model of a spark-ignited (SI) throttle-controlled Caterpillar 3508A gas engine with a hybrid turbocharger. The study investigates the impact of electrical power Power-Take-In/Off from the turbocharger shaft on the engine’s air path dynamics for different use cases, considering transient and steady state phases. We demonstrate that a generator set can benefit from hybrid turbocharger by significantly reducing the engine speed drop and settling time after a load step. While accelerating from 0 to cruise speed, propulsion engines benefit less from hybrid turbocharger, due to risk of compressor surge during low engine speeds. The overall results show that simply adding electric power to the turbocharger shaft during transient phases does not unlock the full potential of hybrid turbocharging for alternatively fueled combustion engines. The implementation of hybrid turbocharging requires careful integration, reconsideration of sizing and matching of turbine and compressor, and the combination with blow-off, blow-by, and waste gate valves to prevent compressor surge. However, the capability for electrical power take-off/in within a larger propulsion and electrical power generation plant context suggests a reduction in spinning reserves and an increase in overall system efficiency during steady state. Thus, implementing hybrid turbocharging can play an important role in the transition to alternative fuels and the reduction of greenhouse gas emissions.

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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.

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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.

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Fuel cell-battery electric drivetrains are attractive alternatives to reduce the shipping emissions. This research focuses on emission-free cargo vessels and provides insight on the design, lifetime operation and costs of hydrogen-hybrid systems, which require further research for increased utilization. A representative round trip is created by analysing one-year operational data, based on load ramps and power frequency. A low-pass filter controller is employed for power distribution. For the lifetime cost analysis, 14 scenarios with varying capital and operational expenses were considered. The Net Present Value of the retrofitted fuel cell-battery propulsion system can be up to $ 2.2 million lower or up to $ 18.8 million higher than the original diesel mechanical configuration, highly dependent on the costs of green hydrogen and carbon taxes. The main propulsion system weights and volumes of the two versions are comparable, but the hydrogen tank (68 tons, 193 m³) poses significant design and safety challenges.

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The current sea margin estimate applied in early ship design, commonly assumed 15-20% extra installed engine power, is not based on calculations, but has nonetheless become an industry standard. These sea margin estimations, applied in early ship design, are insufficiently accurate. This paper evaluates if a data driven approach is suitable to more accurately predict the sea margin in early ship design. Using operational data this method considers the whole operational profile of the vessel not limited to design or calm water conditions. A case study is performed where a data driven model is trained to make power predictions, subsequently this trained model is used to make calm water predictions. This proof of concept illustrates the potential of proposed method to be utilised for sea margin estimations in early ship design.@en

Lightweight structures, once ubiquitous in specific sectors such as aeronautics and space sectors, in recent years, have increasingly attracted the attention of industries that, historically, have not been particularly concerned with structural weight. Ample examples are provided by the civil and automotive industry, in which the paradigm shift towards lower carbon footprint and sustainability prompted new trends characterised by mass reduction, the use of novel materials, and the accounting for large deformations. However, accurately modelling the dynamic behaviour of such structures requires nonlinear mathematical models, which are not widely used in common industrial practices. Reduced-Order Models (ROMs) have emerged as a popular alternative to computationally expensive Finite Element (FE) models, nonetheless, there is still a need to evaluate their effectiveness in accurately modelling strongly nonlinear behaviours. This study investigates the capacity of multiple-degree-of-freedom (MDOF) ROMs to capture and predict the nonlinear behaviour of lightweight structures subjected to large deformations. A novel identification procedure, built on existing linear and nonlinear identification methods, is used to identify an ROM from numerical and experimental data. Being based on the separation of the linear and nonlinear restoring force contributions, the proposed method can be easily embedded in the current industrial practices for the identification of mechanical systems, paving the way to an integrated usage of linear and nonlinear dynamic models. To validate the identified MDOF-ROM, a lightweight structure composed of lumped masses and nonlinear elastic connections is experimentally studied and the numerical and experimental results are compared at different excitation conditions. We demonstrated that the existence of the Nonlinear Restoring Force (NLRF) surface in a reduced subspace corresponds to the presence of local active nonlinearities in the experimental model. This information permits simplifying the nonlinear restoring force function of the ROM, improving the overall identification process. Finally, we showed that the identified ROM accurately represents the nonlinear dynamic behaviour of the experimental test rig and correctly predicts the passage from high-amplitude response to low-amplitude response (jumps) when different levels of excitation are applied to the system, demonstrating the effectiveness of the proposed procedure.

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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.

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The global installed capacity of floating offshore wind turbines is projected to increase by at least 100 times over the next decades. Station-keeping of floating offshore renewable energy devices is achieved through the use of mooring systems. Mooring systems are exposed to a variety of environmental and operational conditions that cause corrosion, abrasion, and fatigue. Regular physical in-service inspections of mooring systems are the golden standard for monitoring their health status. This approach is often expensive, inefficient, and unsafe, and for this reason, researchers are focusing on developing tools for digital solutions for real-time monitoring. Floating offshore renewable energy devices are usually equipped with a wide range of sensors, some low-cost, low/zero maintenance, and easily deployable (e.g., accelerometers on the tower), contrary to others (e.g., direct tension mooring line measurements), producing real-time data streams. In this paper, we propose exploiting the data coming from the first type of sensors for mooring systems health status nowcasting. In particular, we will first rely on state-of-the-art supervised shallow and deep learning models for predicting the health status of the different sections of the mooring lines. Then, since these supervised models require types and amount of data that are seldom available, we will propose new shallow and deep weekly supervised models that require a very small amount of data regarding worn mooring lines. Results will show that these last models can potentially have practical applicability and impact for real-time monitoring of mooring systems in the near future. In order to support our statements, we will make use of data generated with a state-of-the-art digital twin of the mooring system, OrcaFlex, for a floating offshore wind turbine reproducing the physical mechanism of the mooring degradation under different loads and environmental conditions. Results will show errors around 1% in the simplest scenario and errors around 4% in the most challenging one, confirming the potentiality of the proposed approaches.

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Hydrogen-based shipboard power systems (SPS) are gaining prominence as a zero-emission alternative to conventional diesel-fueled systems for reducing the carbon footprint in the maritime sector. Typical designs incorporate fuel cells (FCs) as the main power supply combined with batteries in a DC distribution network. However, the efficient coordination of power generation and storage systems with different characteristics remains a challenge, particularly in topologies with multiple parallel FCs and batteries. This aspect has received limited attention in existing research. To address this challenge, this paper presents a modular approach to the hierarchical control of power generation and storage systems. Dynamic power sharing is achieved using a decentralized strategy that employs bandwidth separation, accounting for the opposing capabilities of each device. Additionally, an energy management strategy (EMS) based on equivalent consumption minimization is realized in this modular framework using a low-bandwidth communication network. The proposed architecture's modular character allows for a flexible power system reconfiguration and extension. The methodology is showcased through simulations using a short-sea cargo vessel as a case study. The results demonstrate that the bandwidth separation ensures the operation of the different technologies within their specified bandwidths, limiting the potential degradation of the FC systems. The addition of the modular EMS shows a fuel-efficient operation of the FC-battery DC SPS and a decrease in the FCs' power gradients, and thereby their aging effect.

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