Early-stage design assessment of loads such as vertical bending moments can be a critical quantity of interest for design exploration. Traditionally, classification societies’ rules are used to calculate such loads. However, relying solely on these rules for designing new vessels may be insufficient, and conducting direct analyses of a large number of designs to support design exploration is computationally infeasible. Currently, key factors such as wave-induced loads are typically evaluated only in later design stages, where a limited number of promising designs are under consideration. This research explores the potential of harnessing multi-fidelity models for early-stage predictions of wave-induced loads, with a specific focus on wave-induced vertical bending moments. As an initial step in this direction, the vertical bending moment analysis was simplified to consider responses in a regular sea state, where the wavelength matches the vessel’s length. The assessed multi-fidelity models include the application of both linear and nonlinear Gaussian processes and compositional kernels to improve predictions of wave-induced loads, specifically focusing on wave-induced vertical bending moments. The case study focuses on the early-stage exploration of the AXE frigates. Multi-fidelity models were constructed using both frequency- and time-domain methods to evaluate the vertical bending moments experienced by the hull. Finally, a critical reflection is provided on how traditional early-stage design processes can be enhanced by integrating multi-fidelity models.

This paper presents a design space exploration (DSE) for the development of an innovative special-purpose installation vessel for tension leg platform (TLP) floating offshore wind turbines. The concept, named “Windchanger,” features an installation deck integrated with the stern design to facilitate turbine lowering and recovery. The DSE model conducts a tradeoff analysis using a parametric model of the hull and installation deck, evaluating both technical feasibility and cost-effectiveness. The model evaluates various scenarios across two demonstration operational areas: the North Sea and the U.S. East Coast. Results evaluated the principle design limitation across various TLP designs, ship concepts, installation design concepts, and operational scenarios. Findings indicate that the transport capacity measured in the number of TLPs, from 1 to 5, has a significant influence on design considerations and economic effectiveness. The overall results show that the Windchanger concept has the potential to be a competitive installation solution.

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.

– 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.

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.

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.