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 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 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 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.

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 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 %.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.