Hydrogen carriers are attractive alternative fuels for the shipping sector. They are zero-emission, have high energy densities, and are safe, available, and easy to handle. Sodium borohydride, potassium borohydride, dibenzyltoluene, n-ethylcarbazole, and ammoniaborane are hydrogen carriers with high theoretical energy densities. The energy density is paramount to implementing hydrogen carriers as a high energy density enables compact and lightweight storage. The effective energy density depends on integrating heat and masses with energy converters. This combination defines the energy efficiency and, thus, the energy density of the system. This paper addresses the effective energy density of the hydrogen carriers, including the dehydrogenation process. Using a 0D model, we combined the five carriers with two types of fuel cells, namely proton exchange membrane (PEM) and solid oxide fuel cells (SOFC), an internal combustion engine and a gas turbine. N-ethylcarbazole and dibenzyltoluene offer medium energy densities, reaching almost 4 MJ/kg. However, the effective energy density of sodium borohydride and ammoniaborane is very high, up to 15 MJ/kg, including the energy converter. This is similar to the energy density of marine diesel oil combined with an internal combustion engine. Thus, we conclude hydrogen carriers are alternative fuels that deserve more attention because of their strong potential to make shipping zero-emission.

This paper proposes Quantum Neural Networks (QNNs) as a data-driven approach for predicting fuel consumption. We utilize various layer architecture designs available in the Torchquantum framework, including both entangled and non-entangled circuit designs. In general, QNNs can achieve comparable Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with significantly fewer trainable parameters. Neither pure QNNs nor hybrid QNN models exhibit the underfitting tendencies seen in classical neural networks (CNNs). Notably, one of the most significant findings of this work is that hybridizing or”dressing” the quantum circuit leads to substantial improvements in RMSE and MAPE for pure QNNs. These promising results suggest potential optimizations for reducing emissions in green shipping.

The integration of methanol power, propulsion and energy systems (PPE) generates uncertainties linked to the selection and sizing of systems, layout design and compliance with strict safety regulations. This paper argues that alternative fuels, such as methanol, should be treated as disruptive innovations, in part due to the uncertainties linked to their implementation. These uncertainties strongly connect to the PPE dimensions and the dependencies among the systems because of integration requirements. Through a model based system engineering inspired approach, the uncertainties are elucidated into relevant inputs for the proposed framework. The authors introduce an uncertainty evaluation framework that uses Monte Carlo simulations to generate the layout design space under uncertainty. The impact of uncertainty on the design is examined through a case study on the layout of a notional engine room. Multiple probability distributions for the PPE dimensions and varied logical architectures – reflecting systems dependencies – are applied to identify patterns in the generated design space. The varied logical architectures influence drastically the dominating solutions of the design space regarding the length. For a 1000-kW notional vessel, under the varied scenarios, the length of the engine room clustered in specific values while the connection costs produced wide value spectrum.

Early-stage design of complex systems is considered by many to be one of the most critical design phases because that is where many of the major decisions are made. The design process typically starts with low-fidelity tools, such as simplified models and reference data, but these prove insufficient for novel designs, necessitating the introduction of high-fidelity tools. This challenge can be tackled through the incorporation of multifidelity models. The application of multifidelity (MF) models in the context of design optimization problems represents a developing area of research. This study proposes incorporating compositional kernels into the autoregressive scheme (AR1) of multifidelity Gaussian processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to five benchmark problems and a simplified design scenario of a cantilever beam. The results demonstrate significant improvement in the prediction accuracy and a reduction in the prediction uncertainty. Additionally, the article offers a critical reflection on scaling up the method and its applicability in early-stage design of complex engineering systems, providing insights into its practical implementation and potential benefits.

Design space reduction decisions made in set-based design use perceptions of feasibility to eliminate unfavorable design solutions from consideration. Perceptions are formed with incomplete information, leaving them susceptible to change if new and conflicting information is made available later in the design process. This paper considers how new information originating from newly sampled design points can alter perceptions of feasibility and introduces a probabilistic and an entropic strategy for quantifying the risk of prematurely eliminating potential design solutions. Emergent designs of automated set-based design simulations gauging this risk are evaluated against ones neglecting it for an analogous design problem. The Python-based simulations have different disciplines randomly explore their design spaces and generate reasonable space reduction propositions, and then they give a design manager the opportunity to check the fragility of reduced design spaces before finalizing any reductions. Gathered results indicate that both the probabilistic and entropic models are able to effectively delay design decisions and help disciplines maintain a higher diversity of design solutions while designer understanding is still growing. Both models effectively delay risky space reductions and encourage a more gradual reduction of design spaces compared to simulations not including fragility checks. Furthermore, as the entropic model takes a more holistic approach by working with the history of perceptions formed in a discipline's design space rather than just the newest perceptions, space remaining and diversity results show it slightly outperforming the probabilistic model.

– Hydrogen carriers, such as liquid organic hydrogen carriers (LOHCs) and borohydrides, are promising zero-emission alternative fuels for ships. Bringing these hydrogen carriers on board, however, creates new challenges. A major challenge is their spill behaviour. Knowing the spill behaviour is paramount to avoid large-scale environmental disasters. This paper investigates the spill behaviour of four hydrogen carriers (and their conjugates): sodium borohydride, ammonia borane, dibenzyltoluene, and n-ethylcarbazole. The hydrogen carriers were all dissolved in artificial seawater to test their behaviour. Sodium borohydride reacts with seawater, as it also reacts with pure water. However, contrary to expectations, it reacts faster with seawater than regular water. The reaction mechanism behind this is unknown. Ammonia borane does not visibly react with normal water or with seawater. Dibenzyltoluene sinks and forms tiny bubbles which are easily perturbed. Unfortunately, perhydro dibenzyltoluene could not be tested due to technical problems. N-ethylcarbazole breaks up into smaller pieces and predominantly stays afloat, likely due to the surface tension of water. Perhydro n-ethylcarbazole floats but is barely visible in seawater due to its transparency. Preventive measures must be established to avoid large-scale spills if these substances are utilised on ships, as they are likely challenging to clean up.

This paper proposes a data-driven approach to reduce emissions in international shipping, aligning with the IMO's goal of achieving net-zero greenhouse gas emissions by around 2050. Digital twins (DTs) offer promise for maritime decarbonization due to their simulation and big data handling capabilities. However, fully realizing DTs for new-build is by definition challenging as it requires a real-time data connection. Thus, the research begins with retrofitting existing ships using operational data collected through Bunker Delivery Notes (BDNs), a mandatory method for larger ships since January 2019. The proposed framework constructs digital models to support the retrofit DT, that are tested on a 300m bulk carrier. A fuel consumption model is built using a gray box approach, while various wind-assisted ship propulsion systems are modeled using a white box approach. The study evaluates the design implications and emissions reduction potential of implementing these systems.

The development of the global COVID-19 pandemic from 2020 onward has had significant impact on the world and specifically the maritime industry. Striking examples were COVID-19 outbreaks onboard the Diamond Princess cruise vessel and the U.S.S. Theodore Roosevelt aircraft carrier at the start of the pandemic. Contagious disease management onboard large passenger ships remains a complex issue, amplified by the international character of the industry, confined environment and shared facilities. This paper therefore presents an integrated infection and crowd behavior model used to calculate agent-specific infection risk, incorporating guest and crew circulation through a passenger ship layout. The integrated model is used to investigate the effect of ship layout design, capacity reduction and mask wearing on COVID-19 airborne infection risk onboard large passenger vessels.

Early-stage design exploration is crucial since most of the major design decision are locked-in and only small design modifications are possible at later stages. To assess the performance of the various design candidates while performing design exploration, there are available methods and tools of various fidelities. These methods can be combined to form a multi-fidelity (MF) framework that guarantees accuracy through the high-fidelity model and achieves faster computational speeds through low-fidelity models. The present study proposes the adoption of information-theoretic entropy to improve a MF design framework based on Gaussian Processes (GPs). Entropy quantifies the uncertainty associated with the prediction of the design space. We propose using this uncertainty metric both as a criterion to determine whether further designs should be sampled to construct a reliable approximation of the design space and as a criterion to establish in which optimization step the optimization of the covariance matrix for the MF-GPs should be performed. The approach was tested to benchmark analytical functions and to a ship design problem of an AXEfrigate. The approach holds potential in practical applications, as it aids in the determination of whether additional resources should be allocated for high-fidelity analysis to support early-stage exploration.

Reducing the use of fossil fuels in shipping requires new, alternative maritime fuels. Hydrogen carriers offer a safe and energy-dense solution for storing hydrogen, a zero-emission alternative fuel. This research focuses on ammonia borane, NaBH4, n-ethylcarbazole and dibenzyltoluene. Applying hydrogen carriers influences ship design significantly, as they require additional specialised equipment to remove hydrogen from the hydrogen carrier. This research estimates the size of the equipment. As this equipment will need to be stored and maintained on the ship, the exact sizing and sequence of the additional equipment will likely influence ship design. Results show that the reactor size is significant for all hydrogen carriers. The mixing tank is considerably sized for NaBH4 and ammonia borane, while the heat exchangers are large for dibenzyltoluene and n-ethylcarbazole.

The ongoing technological development of methanol energy converters (EC) towards decarbonization means that their dimensions and performance characteristics will be continually updated during the lifecycle of vessels currently designed. These advancements influence the ease of EC integration within the general arrangement of the vessel. The decision to switch from an internal combustion engine to a fuel cell or a hybrid configuration depends both (1) the technology adoption costs (i.e. CAPEX, OPEX) of the EC and (2)
on the effect of EC on the actual engine room layout. The state-of-the-art literature has typically addressed these two challenges separately. This study proposes a design method to bridge these two fields by combining the use of (1) Markov decision processes to assess uncertain future methanol EC developments during the vessel lifecycle and (2) a generative probabilistic layout algorithm to quantify the risks associated with the EC systems layout integration. The case study identifies the drivers behind the EC technology choice during the lifecycle of a notional yacht vessel.

Keynote. The evolution of ship design from a manual toward a computer-aided, digital approach has been drastic after the 1970s, with the explosive development of computer hardware and software systems. In today’s era of smart digitalization in the frame of Industry 4.0, recently introduced digital/software tools and systems increase the efficiency and quality of the life-cycle ship design process, but also the operational complexity and the demand for proper training of users of software platforms. Parametric optimisation and simulation-driven design, product lifecycle management, digital twins and artificial intelligence are nowadays frequently used by the maritime industry during the commissioning/quality control activities and in the various phases of ship design, ship operation and ship production. This paper presents an overview of notable developments in the above areas and the way ahead to respond to present and future challenges of the maritime industry.

This paper examines the technical and economic influence of CO2 reduction measures on the design and operation of Dutch beam trawlers. This is done by means of a parametric model used to assess the influence on the overall design of the vessel. Technical feasibility is determined by meeting operational effectiveness requirements, maximum added draught, maximum added length, and a reduction of CO2 emission by at least 40%. Secondly the model evaluates the new energy carrier and fish storage layout as a result of additional required volume. Additional volume is gained within the net store, fish hold, or by hull extension. Additionally, various propeller configurations, waste heat recovery, and regenerative braking systems are explored to reduce energy consumption. The economic performance is assessed using yearly operational requirements, capital expenses of configuration, and total cost of ownership.

This paper investigates the feasibility of iron powder energy generation systems on board a semi-submersible crane vessel. This is done using a design model that integrates design information and a simulated mission profile to determine a hybrid iron powder setup split. This setup is then placed within a set of vessel designs to calculate a base level feasibility looking at the draft, stability, and emissions decrease. For those concepts that were technically feasible, the new hybrid iron powder setup contributed to a reduction of CO2 up to 25-50% and a reduction of NOx emissions between 15-50%, depending on the mission profile.

New alternative maritime fuels are required to reduce or eliminate the use of fossil fuels in shipping. This need extends across all shipping industry sectors, including the Navy. However, Navy vessels comply with unique requirements compared to vessels within the general shipping industry. Increased safety is one of these and alternative fuels like ammonia may not meet these safety standards. Other alternative fuels, such as methanol, require additional mitigating measures like cofferdams. However, solid hydrogen carriers, such as ammonia borane and sodium borohydride, will most likely not require further safety measures and could even increase safety. These solid hydrogen carriers have a powder-like nature, high flashpoint, and high energy density, of about half that of MDO. They could be suitable not only as an alternative fuel but also as a damper to reduce impact consequences. This research aims to explore whether these two alternative fuels can mitigate the impact and what the consequences of this impact will be on the survivability of a ship. Both impact with and without heat will be taken into account. Ship collisions happen to civilian ships and naval ships alike, so heatless impact remains a vital topic of study. Additionally, the current geopolitical state of affairs may also require civilian ships to sail through more hostile waters than before, possibly even experiencing impacts where heat is generated. The research will follow a conceptual approach based on chemistry and chemical reactions. This approach allows for the evaluation of alternative fuels for various ship types.

This paper compares two 2XL monopile installation methods: at the leeward side of the heavy lift crane vessel and in the recess at the stern of the vessel. The multi-body system of the vessel, monopile, crane, and mission equipment induces interaction and resonance behaviour. Operational limits are assessed at the crane tip and pile gripper during upending and lowering of the monopile. Stern installation provides a larger operability window during upending compared to side installation. During the lowering stage, the operability depends on the monopile submergence: side and stern installation provide a comparable operability. Considering both stages, stern installation shows promising results.

This paper proposes a grey-box modelling approach to predict marine biofouling growth and its effects on ship performance. The approach combines empirical or experimental-based white-box models with data-driven black-box models. First, a white-box model is built to predict ship resistance considering a bare hull. This prediction is based on calm water resistance, wind, waves, and temperature differences. Subsequently, marine biofouling growth is predicted using an experimental model that estimates the level of roughness on the ship hull. Finally, a deep extreme learning machine is used as a black-box model, employing a feedforward neural network technique. To test the approach, a superyacht case study was selected as a category of vessel heavily exposed to fouling. The study used a 2-year dataset obtained through a collaboration with Feadship. Results showed that the black-box approach outperforms the white-box approach in predictive capabilities. However, when the knowledge encapsulated in the white-box model is included in the grey-box approach, the model shows the highest prediction accuracy achieved by leveraging less historical data. This study demonstrates the potential of the proposed grey-box approach to accurately predict marine biofouling growth and its effects on ship performance, which can benefit ship operators and designers in improving operational efficiency and reducing maintenance costs.

This paper addresses the growing offshore wind market's demand for larger turbines in deeper waters by highlighting limitations in existing installation solutions and proposing a new concept with a floating monohull, named Moonshot, which will thus be different than traditional jack-up or semi-submersible crane installation vessel options. This paper discusses the design process, which combines Ulstein Rotterdam’s Controlled Innovation and Blended Design to develop the concept. This process is used to explore various market scenarios to determine optimal vessel parameters. Results demonstrate how optimizing for financial performance or seakeeping behavior impacts the design. Moonshot's initial parameters are established, and its performance is compared to existing installation solutions.

Perceptions of feasibility in design spaces are susceptible to change if new and conflicting information becomes available later. Design space reduction decisions made in set-based design can amplify vulnerability to new information if remaining design spaces and present perceptions are unable to adapt. This paper considers different ways new information can alter perceptions of feasibility for complex design problems and introduces an early, probabilistic strategy for quantifying the risk of eliminating potential design solutions based on the vulnerability of remaining design spaces to new information. Emergent designs of a set-based design process gauging this risk are evaluated against one neglecting it for an analogous design problem. Early results indicate that the probabilistic model is able to effectively delay design decisions and prevent lock-in while design space understanding is still growing.

The prediction of the statutory attained subdivision index is a challenging issue for the initial design of ships due to the design freedom offered by a probabilistic damage stability assessment. To this end, optimisation techniques integrated with a parametric model of the internal layout may generate a preliminary subdivision design, fulfilling damage stability regulations and cargo volume requirements. The present study explores using a multi-objective constrained optimisation algorithm coupled with a parametric model of a single hold cargo vessel, first investigating two design goal alternatives and secondly performing a global sensitivity analysis on the design variables for the most promising solution. The adoption, in parallel, of state-of-the-art practices shows the validity of the obtained solutions and the time benefits for designers. Nonetheless, the non-linear nature of probabilistic damage stability does not allow for clearly identifying the most impactful parameters on the attained survivability index.