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Application of multi-objective Bayesian shape optimisation to a sharp-heeled Kaplan draft tube
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G. R. Tabor,
J. E. Fieldsend,
R. M. Everson
Optimization and Engineering, Volume: 23, Pages: 689 - 716
Swansea University Author:
Alma Rahat
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© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License
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DOI (Published version): 10.1007/s11081-021-09602-6
Abstract
The draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an...
| Published in: | Optimization and Engineering |
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| ISSN: | 1389-4420 1573-2924 |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa56256 |
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| Abstract: |
The draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an elbow-type draft tube is undertaken, combining Computational Fluid Dynamics and a multi-objective Bayesian methodology. The chosen design objectives were to maximise pressure recovery, and minimise wall-frictional losses along the geometry. The design variables were chosen to explore potential new designs, using a series of subdivision-curves and splines on the inflow cone, outer-heel, and diffuser. The optimisation run was performed under part-load for the Kaplan turbine. The design with the lowest energy-loss identified on the Pareto-front was found to have a straight tapered diffuser, chamfered heel, and a convex inflow cone. Analysis of the performance quantities showed the typically used energy-loss factor and pressure recovery were highly correlated in cases of constant outflow cross-sections, and therefore unsuitable for use of multi-objective optimisation. Finally, a number of designs were tested over a range of discharges. From this it was found that reducing the heel size increased the efficiency over a wider operating range. |
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| Keywords: |
Hölleforsen–Kaplan draft tube; Bayesian optimisation; Multi-objective optimisation; Shape optimisation; Sub-division curves |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by the UK Engineering and Physical Sciences Research Council [grant number EP/M017915/1]. |
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