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.@en

This paper describes two new modular ship design activities for graduate education at Delft University of Technology that have been developed during COVID. First, a new 2-hour hybrid format (in-person and virtual participation) game was designed to teach students modular design for offshore support vessels (OSVs). Second, an 8-week MSc-level ship design project was redeveloped to cover the design of a small fleet of modular OSVs for offshore wind. The paper discusses the drivers behind these new design educational activities, the details of the activities themselves, and concludes with lessons learned focused on improving graduate education for masters students studying ship design.

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This paper presents and demonstrates a new design thinking framework for early stage complex ship design, called the Design Knowledge Management Square (DKMS) framework. The DKMS framework provides a structure that explicitly incorporates the collaborative nature of complex ship design, contrary to other models or frameworks that primarily focus on the technical integration of tools and methods to describe early stage complex ship design. The DKMS framework is applied to three case studies: 1) multi-disciplinary early stage design of complex ships, 2) the integration of concept design generation and analysis methods, and 3) the application of design rationale to support collaborative design decision-making. The case studies show that the DKMS framework provides added value by explicitly describing both the collaborative and technical nature of complex ship design. Thereby the framework helps to analyse, support, and understand complex ship design.@en

The aim of this paper is to discuss the challenges associated with the early-stage design of novel and reliable vessels, and discuss some of the expected benefits of the application of multi-fidelity models in addressing some of their early-stage design problems. Traditionally, early-stage design tools are computationally cheap, but lack in accuracy. However, for the design of novel vessels, these tools are not sufficient. The first part of the paper discusses the challenges associated with the design of novel vessels. The second part of the paper focuses on a literature review on the application of the multi-fidelity models to the design of complex engineering systems. Finally, the most promising methods are identified and discussed.

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This paper proposes a parametric modelling method based on Knowledge Based Engineering (KBE) for an LNG bunkering vessel (LNGBV). Parametric models aim to define the geometry and main systems configuration of the vessel starting from the principle that the vessel should be able to perform its mission successfully. The adoption of KBE in combination with parametric modelling is expected to improve the current practice by automating the generation of the parametric models based on the design requirements. The results showed that different design alternatives can be rapidly generated which in turn gives the possibility to the designer to perform a wide exploration of the preliminary design space.@en