- Title
- Comparison of multiple surrogates for 3D CFD model in tidal farm optimisation
- Creator
- Moore, William; Mala-Jetmarova, Helena; Gebreslassie, Mulualem; Tabor, Gavin; Belmont, Michael; Savic, Dragan
- Date
- 2016
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/104225
- Identifier
- vital:11042
- Identifier
-
https://doi.org/10.1016/j.proeng.2016.07.523
- Identifier
- ISBN:18777058 (ISSN)
- Abstract
- Marine currents have been identified as a considerable renewable energy source. Therefore, in recent years, research on optimising tidal stream farm layouts in order to maximise power output has emerged. Traditionally, computational fluid dynamics (CFD) models are used to model power output, but their computational cost is prohibitive within an optimisation algorithm. This paper uses surrogate models in place of CFD simulations to optimise the layout of tidal stream farm layouts. Surrogates are functions which are designed to emulate the behaviour of other models with radically reduced computational expense. Two surrogate models are applied and compared: artificial neural network (ANN) and k-nearest neighbours regression (k-NN). We measure their suitability by four criteria: accuracy, efficiency, robustness and performance within an optimisation algorithm. The results reveal that the ANN surrogate is superior in every criteria to the k-NN surrogate. However, the k-NN surrogate is also able to perform adequate optimisation. Finally, we demonstrate that optimisation relying solely on surrogate models is a viable approach, with dramatically reduced computational expense of optimisation. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.; Procedia Engineering
- Publisher
- Elsevier Ltd
- Relation
- 12th International Conference on Hydroinformatics - Smart Water for the Future, HIC 2016; Songdo Convensialncheon, South Korea; 21st-26th August 2016; published in Procedia Engineering Vol. 154, p. 1132-1139
- Rights
- Copyright © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- MD Multidisciplinary; Artificial neural network; k-nearest neighbours regression; Surrogate model; Tidal stream farm layout
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