Data underlying chapter 3 of the PhD dissertation: Multi-fidelity probabilistic design framework for early-stage design of novel vessels
DOI: 10.4121/1dcda9bd-4ce6-4e0c-9b84-9292d4e101d0
Dataset
Licence CC BY 4.0
This repository contains the code and data supporting the results presented in Chapter 3 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Multi-fidelity design framework integrating compositional kernels to facilitate early-stage design exploration of complex systems". The research explores the integration of compositional kernels into the autoregressive scheme (AR1) of Multi-Fidelity Gaussian Processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to 5 benchmark problems and a simplified design scenario of a cantilever beam.
The data include: (1) the Ansys model of the cantilever beam, (2) the simulation data, (3) the data associated with the analyzed cases, and (4)the Python scripts can be found in this gitlab repository.
History
- 2024-11-25 first online
- 2025-02-20 published, posted
Publisher
4TU.ResearchDataAssociated peer-reviewed publication
Multi-Fidelity Design Framework Integrating Compositional Kernels to Facilitate Early-Stage Design Exploration of Complex SystemsFunding
- 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.
Organizations
TU Delft, Faculty of Mechanical Engineering, Department of Maritime and Transport TechnologyDATA
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- 2,038 bytesMD5:
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README.txt - 6,626 bytesMD5:
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datasets.zip - 1,338,370,478 bytesMD5:
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examined_cases.zip - 341,915 bytesMD5:
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parametric_model.zip -
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