Nikoleta Dimitra Charisi,
Emile Defer,
Hans Hopman,
Austin Kana,
Version 1 of Dataset published 2024 via 4TU.ResearchData
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