E-Thesis 64 views 19 downloads
Computational Heat Transfer Optimisation in Nuclear Fusion Reactors Using Data-Driven Machine Learning / DANIELA GALEANA
Swansea University Author: DANIELA GALEANA
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PDF | E-Thesis – open access
Copyright: the author, Daniela M. Segura Saleana, 2025. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0)
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DOI (Published version): 10.23889/SUThesis.71082
Abstract
In the field of energy applied to fluid dynamics, the net power output of a system is dependant upon the thermal and hydraulic behaviour of the flow in interaction with surrounding objects.For cooling systems applications, the interest relies on the understanding and optimisation of the Thermo-Hydrauli...
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Swansea
2025
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| Institution: | Swansea University |
| Degree level: | Doctoral |
| Degree name: | Ph.D |
| Supervisor: | Gil, A. J. |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa71082 |
| Abstract: |
In the field of energy applied to fluid dynamics, the net power output of a system is dependant upon the thermal and hydraulic behaviour of the flow in interaction with surrounding objects.For cooling systems applications, the interest relies on the understanding and optimisation of the Thermo-Hydraulic Performance (THP) research. The THP optimisation relevant to this research is that of shape parametrisation. It has been demonstrated that utilising a biomimetic geometry on the fluid-facing surface of a pipe, based on the skin of fast sharks,improves hydraulic performance. However, the design of such geometry introduces a number of challenges that need to be addressed. First, the thermal and hydraulic mechanisms of the flow have a coupled nature, with a trade-off between them leading to a Multi-Objective Optimisation (MOO) problem. Second, the use of Computational Fluid Dynamics (CFD)to analyse the coupled flow behaviour. This is a cost-effective alternative to experimental testing and a favourable tool for geometry parametrisation. However, CFD implies a back-and-forth process between design and modelling over several iterations of shape parameters, which can make the development of optimal geometries highly time-consuming for the required level of accuracy. Therefore, industry demands for increasing levels of accuracy and the need for optimising the design process itself prompts the search for new means to undertake these tasks.In the ambition to address the above challenges within the computational design process, this thesis puts forward a comparative data-driven computational framework with an associated open-source software. The software generates both a database containing CFD high-fidelity models of parametrised geometries, as well as surrogate models that are trained with such data to approximate the THP optimisation process. The thesis puts forward three novelties. First, the study of the influence that simplified biomimetic geometries have in the coupled thermo-hydraulic behaviour of the flow in pipes. Second, a comparative analysis using alternative Machine Learning (ML) techniques in order to identify the advantages and drawbacks of each algorithm across different approaches. The considered ML methods are Radial Basis Function (RBF) interpolation, Neural Networks (NN) and Gaussian Processes (GP),with the addition of Proper Orthogonal Decomposition (POD) as a dimensional reduction method prior to the models’ training to potentially ease the computational expense. Third, the framework’s main direction is to use ML algorithms to enrich the search for an optimal geometry within the considered parametric space. The efforts made to reach the objectives of this research have resulted into the open-source software Hammerhead integrated environment for high-fidelity and ML based computational design optimisation. This new framework is developed and implemented using Python3 language, including compatibility with OpenFOAM open-source CFD platform. |
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| Keywords: |
CFD, machine learning, POD, PCA, Gaussian process, neural networks, optimisation, heat transfer, fluid mechanics |
| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC |