International Journal of Fluid Machinery and Systems
Online ISSN : 1882-9554
ISSN-L : 1882-9554
Original Papers
Physics-based Surrogate Optimization of Francis Turbine Runner Blades, Using Mesh Adaptive Direct Search and Evolutionary Algorithms
Salman Bahrami Christophe TribesSven von FellenbergThi C. VuFrançois Guibault
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JOURNAL FREE ACCESS

2015 Volume 8 Issue 3 Pages 209-219

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Abstract

A robust multi-fidelity optimization methodology has been developed, focusing on efficiently handling industrial runner design of hydraulic Francis turbines. The computational task is split between low- and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a physics-based surrogate optimization loop manages a large number of iterative optimization evaluations. Two derivative-free optimization methods use an inviscid flow solver as a physics-based surrogate to obtain the main characteristics of a good design in a relatively fast iterative process. The case study of a runner design for a low-head Francis turbine indicates advantages of integrating two derivative-free optimization algorithms with different local- and global search capabilities.

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© 2015 Turbomachinery Society of Japan, Korean Fluid Machinery Association, Chinese Society of Engineering Thermophysics, IAHR
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