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Hydraulic turbine diffuser shape optimization by multiple surrogate model approximations of pareto fronts
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.ORCID iD: 0000-0002-1033-0244
Department of Mechanical and Aerospace Engineering, University of Florida.
Department of Mechanical and Aerospace Engineering, University of Florida.
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2007 (English)In: Journal of Fluids Engineering - Trancactions of The ASME, ISSN 0098-2202, E-ISSN 1528-901X, Vol. 129, no 9, p. 1228-1240Article in journal (Refereed) Published
Abstract [en]

A multiple surrogate-based optimization strategy in conjunction with an evolutionary algorithm has been employed to optimize the shape of a simplified hydraulic turbine diffuser utilizing three-dimensional Reynolds-averaged Navier-Stokes computational fluid dynamics solutions. Specifically, the diffuser performance is optimized by changing five geometric design variables to maximize the average pressure recovery factor for two inlet boundary conditions with different swirl, corresponding to different operating modes of the hydraulic turbine. Polynomial response surfaces and radial basis neural networks are used as surrogates, while a hybrid formulation of the NSGA-IIa evolutionary algorithm and a ε-constraint strategy is applied to construct the Pareto front from the two surrogates. The proposed optimization framework drastically reduces the computational load of the problem, compared to solely utilizing an evolutionary algorithm. For the present problem, the radial basis neural networks are more accurate near the Pareto front while the response surface performs better in regions away from it. By using a local resampling updating scheme the fidelity of both surrogates is improved, especially near the Pareto front. The optimal design yields larger wall angles, nonaxisymmetrical shapes, and delay in wall separation, resulting in 14.4% and 8.9% improvement, respectively, for the two inlet boundary conditions.

Place, publisher, year, edition, pages
2007. Vol. 129, no 9, p. 1228-1240
National Category
Fluid Mechanics and Acoustics
Research subject
Fluid Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-12464DOI: 10.1115/1.2754324ISI: 000249352700015Scopus ID: 2-s2.0-34748843385Local ID: b9e586b0-a59c-11db-9811-000ea68e967bOAI: oai:DiVA.org:ltu-12464DiVA, id: diva2:985415
Note
Validerad; 2007; 20070109 (ysko)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Marjavaara, DanielLundström, Staffan

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