10.4121/fc643c31-5428-48dc-bcf3-c8a24d49331a.v1

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Data underlying chapter 4 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels Dataset

Nikoleta Dimitra Charisi, Emile Defer, Hans Hopman, Austin Kana,
This repository contains the code and data supporting the results presented in Chapter 4 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Multi-fidelity design framework to support early-stage design exploration of the AXE frigates: the vertical bending moment case". The 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. The assessed 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.
The data include: (1) the parametric model developed in Rhino and Grasshopper used to generate the hull mesh, (2) the

Citation

Charisi, N. D., Defer, E., Hopman, H., & Kana, A. (2024). Data underlying chapter 4 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels (Version 1) [Data set]. 4TU.ResearchData. https://doi.org/10.4121/FC643C31-5428-48DC-BCF3-C8A24D49331A.V1