The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/459cfd14-ab90-4fbb-9a1f-2cbd96baaa1a

Charisi, Nikoleta Dimitra; Hopman, Hans; Kana, Austin (2024): Data underlying chapter 6 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/459cfd14-ab90-4fbb-9a1f-2cbd96baaa1a.v1

Dataset

• Mechanical Engineering
• Maritime Engineering
• Engineering

Complex vessels, Compositional kernels, Design exploration, Early-stage design, Entropy, Gaussian Processes, Information theory, Multi-fidelity models

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Nikoleta Dimitra Charisi , Hans Hopman , Austin Kana

This repository contains the code and data supporting the results presented in Chapter 6 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Leveraging the concept of information-theoretic entropy to improve a multi-fidelity design framework for early-stage design exploration of complex vessels". The research explores the adoption of information-theoretic entropy to improve a multi-fidelity design framework based on Gaussian Processes. Entropy quantifies the uncertainty associated with the prediction of the design space. The research focuses on 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 multi-fidelity Gaussian Processes should be performed.

The data include: (1) the parametric model developed in Rhino and Grasshopper used to generate the hull mesh, (2) the simulation dataset, (3) the data associated with the analyzed analytical cases, and (4)the Python scripts for the development of the MF models can be found in this gitlab repository. The analysis solvers used to calculate the vertical bending moments for calculating the vertical bending moments are not included in this repository.

• 2024-11-25 first online, published, posted

4TU.ResearchData

Leveraging the concept of information-theoretic entropy to improve a multi-fidelity design framework for early-stage design exploration of complex vessels

• This publication is part of the project ‘‘Multi- fidelity Probabilistic Design Framework for Complex Marine Structures, The Netherlands’’ (project number TWM.BL.019.007) of the research programme ‘‘Topsector Water & Maritime: the Blue route’’, which is (partly) financed by the Dutch Research Council (NWO) and Stichting Bijlboegfonds, The Netherlands.

TU Delft, Faculty of Mechanical Engineering, Department of Maritime and Transport Technology