Multi-fidelity design optimization of Francis turbine runner blades

Bahrami, Salman; Tribes, Christophe; Fellenberg, S von; Vu, Thi Thu Ha; Guibault, François

doi:10.1088/1755-1315/22/1/012029

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…Besides, a significant reduction of pressure pulsation and an increase of shedding frequency were found as well, which is in a good agreement with the research of Ref. [15].…”

Section: Results Of Factorial Experimentssupporting

confidence: 92%

“…For example, Viviano and Musumeci et al [7][8][9] proposed simplified numerical models, which simulate turbulence by means of eddy viscosity formulation, to study the mechanism of waves. But when it comes to optimal design, computational fluid dynamics (CFD) turns out to be a better choice because it is more practical and it can overcome the limitations of experimental conditions [10][11][12][13][14][15][16][17][18]. For instance, Zhang et al [10] performed an investigation of two degrees of freedom on vortex-induced vibration under the wakes interference of an oscillating airfoil.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

et al. 2017

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Abstract:In this paper, a new methodology is proposed to reduce the vortex-induced vibration (VIV) and improve the performance of the stay vane in a 200-MW Francis turbine. The process can be divided into two parts. Firstly, a diagnosis method for stay vane vibration based on field experiments and a finite element method (FEM) is presented. It is found that the resonance between the Kármán vortex and the stay vane is the main cause for the undesired vibration. Then, we focus on establishing an intelligent optimization model of the stay vane's trailing edge profile. To this end, an approach combining factorial experiments, extreme learning machine (ELM) and particle swarm optimization (PSO) is implemented. Three kinds of improved profiles of the stay vane are proposed and compared. Finally, the profile with a Donaldson trailing edge is adopted as the best solution for the stay vane, and verifications such as computational fluid dynamics (CFD) simulations, structural analysis and fatigue analysis are performed to validate the optimized geometry.

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“…Besides, a significant reduction of pressure pulsation and an increase of shedding frequency were found as well, which is in a good agreement with the research of Ref. [15].…”

Section: Results Of Factorial Experimentssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

et al. 2017

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“…However, it is easily adapted for singleobjective optimizations, as described in the article by Bahrami et al (2014). While the focus of that article was a low-head Francis runner test case without emphasis on the filtering process, the current article concentrates mostly on the TBFA and its steps.…”

Section: Territorial-based Filtering Algorithmmentioning

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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“…In section 3, a case study is presented using a lowhead Francis runner. Even though we presented the multi-fidelity methodology using the same test case in 27th IAHR Symposium on Hydraulic Machinery and Systems [9], the current enhanced paper focuses on how the exploration capabilities of optimization methods affect the optimization efficiency and robustness to obtain good Francis runner designs. Mesh adaptive direct search (MADS) and evolutionary algorithm have been employed, which are mostly well-known as good local-and global search methods respectively.…”

Section: Fig 1 Runner Design Loop Interactionsmentioning

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-fidelity design optimization of Francis turbine runner blades

Cited by 8 publications

References 5 publications

An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

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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