Multi-Objective Optimization of Runner Blades Using a Multi-Fidelity Algorithm

Bahrami, Salman; Tribes, Christophe; Devals, Christophe; Vu, Thi Thu Ha; Guibault, François

doi:10.1115/power2013-98149

Cited by 2 publications

(3 citation statements)

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“…Figure 5 shows the optimization flowchart, which is described in the following subsections. The multi-level optimization methodology and its formulation have been presented in previous publications (Bahrami et al 2013(Bahrami et al , 2016.…”

Section: Test Case 1: Hydraulic Turbine Runner Bladesmentioning

confidence: 99%

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

Bahrami

Khelghatibana

Tribes

et al. 2016

Engineering Optimization

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A territorial-based filtering algorithm (TBFA) is proposed as an integration tool in a multi-level design optimization methodology. The design evaluation burden is split between low-and high-cost levels in order to properly balance the cost and required accuracy in different design stages, based on the characteristics and requirements of the case at hand. TBFA is in charge of connecting those levels by selecting a given number of geometrically different promising solutions from the low-cost level to be evaluated in the high-cost level. Two test case studies, a Francis runner and a transonic fan rotor, have demonstrated the robustness and functionality of TBFA in real industrial optimization problems. ARTICLE HISTORY

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Section: Test Case 1: Hydraulic Turbine Runner Bladesmentioning

confidence: 99%

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

Bahrami

Khelghatibana

Tribes

et al. 2016

Engineering Optimization

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“…For instance, we presented the results of a bi-objective low-fidelity optimization employed in the developed multi-fidelity methodology [17]. Also, EASY has demonstrated its success to handle a multi-objective Francis runner optimization problem [14].…”

Section: Optimization Featuresmentioning

confidence: 99%

“…However, if there was a significant deviation from the targeted peak position, the operating condition used in the low-fidelity optimization constraints should be corrected in the next optimization step. We applied two linear corrections previously in optimizing a medium high-head Francis runner within three optimization overall loops [17]. Also, off-design operating points can be considered by adding new constraints and/or objectives dedicated to those operating conditions such as [21].…”

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confidence: 99%

Physics-based Surrogate Optimization of Francis Turbine Runner Blades, Using Mesh Adaptive Direct Search and Evolutionary Algorithms

Bahrami

Tribes

Fellenberg

et al. 2015

International Journal of Fluid Machinery and Systems

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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. Keywords:Physics-based surrogate optimization, Francis turbine runner blade, multi-fidelity algorithm Hydraulic Turbine Design Optimization ProcessBig changes in global energy demand, increasing environmental concerns, and growth potential of cost-efficient hydroelectric energy, have recently resulted in more demand to design hydraulic turbines which are more efficient and durable. As design challenges are getting more complex, runner designers rely more than ever on engineering and simulation tools, especially computational fluid dynamics (CFD), to obtain reliable designs with a competitive time and cost. Although runner designers already employ CFD tools to evaluate their designs, there is a strong need to integrate CFD analyses more tightly in the design chain using efficient optimization methods to obtain more efficient design processes.The full range of CFD methods have been utilized in the optimization of hydraulic turbine runner blades. Low-fidelity inviscid models (e.g. potential flow) have been employed by some researches such as Holmes and McNabb [1]. However, they are not accurate enough in their prediction of flow behavior, mainly due to lack of physics. High-fidelity viscous models have been used alone to optimize the runner as well (e.g. using turbulent RANS solvers, by Franco-Nava et al. [2] and Pilev et al. [3]); but they are too expensive and slow for iterative industrial runner design processes. To reduce high-fidelity analyses in the optimization loop, surrogatebased optimization approaches have been increasingly employed by researchers, using either mathematical surrogates or physic-based surrogates. Mathematical surrogates are computationally inexpensive approximation models constructed from a given number of highfidelity evaluations. For instance, artificial neural network was applied by Derakhshan et al. [4] to reduce Navier-Stokes solver calls during the optimization of a low-head axial hydro turbine by an evolutionary algorithm. Another popular mathematical surrogate, radial basis functions, was employed by Georgopoulou et al. [5].Although those mathematical surrogates have been used for blade shape optimizations, they still require a large number of high-

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Multi-Objective Optimization of Runner Blades Using a Multi-Fidelity Algorithm

Cited by 2 publications

References 0 publications

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

Physics-based Surrogate Optimization of Francis Turbine Runner Blades, Using Mesh Adaptive Direct Search and Evolutionary Algorithms

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