Preprint Article Version 1 This version not peer reviewed

Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II

Version 1 : Received: 22 April 2017 / Approved: 24 April 2017 / Online: 24 April 2017 (10:52:28 CEST)

How to cite: Mun, C.N.; Ba, D.C.; Yue, X.J.; Kim, M.I. Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II. Preprints 2017, 2017040148 (doi: 10.20944/preprints201704.0148.v1). Mun, C.N.; Ba, D.C.; Yue, X.J.; Kim, M.I. Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II. Preprints 2017, 2017040148 (doi: 10.20944/preprints201704.0148.v1).

Abstract

In order to improve the performance of the draft tube in hydraulic turbine, a multi–objective optimization method for the draft tube is developed by combining the design of experiment (DOE), the radial basis function (RBF) and the non–dominated sorting genetic algorithm (NSGA–II) in this paper. The geometrical design variables of the median section in the draft tube and the cross section in its exit diffuser are considered as design parameters in this optimization, which objective function is to maximize the pressure recovery factor (Cp) and minimize the energy loss coefficient (ζ). The limited numbers of design matrix required for the shape optimization of the draft tube is generated by optimal Latin hypercube (OLH) method of the DOE technique, of which performances are evaluated through computational fluid dynamic (CFD) numerical simulation. For reducing of the computational consumption, the approximate model is used based on the RBF. The Pareto optimal solutions are finally performed using the NSGA–II for obtaining the best geometrical parameters of the draft tube. The optimization result of the draft tube shows a marked performance improvement over the original, which verifies the theoretical validity and feasibility of the proposed method in this paper.

Subject Areas

francis turbine; draft tube; optimization design; experiment of design; non–dominated sorting genetic algorithm

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