Multiobjective optimal design of runner blade using efficiency and draft tube pulsation criteria

Pilev, I M; Sotnikov, Artem; Rigin, V E; Semenova, A.V.; Cherny, S. G.; Чирков, Д. В.; Bannikov, Denis; Skorospelov, V. A.

doi:10.1088/1755-1315/15/3/032003

Cited by 7 publications

(4 citation statements)

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“…Energy losses in these elements depend significantly on turbine type, specific speed and operating point. When designing a turbine, great attention is usually paid to the runner shape since it is the most There are many papers dedicated to the development and application of runner shape optimization tools, based on CFD simulations [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Optimization design of the elbow draft tube of the hydraulic turbine

Semenova¹,

Чирков

Skorospelov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The draft tube of the hydraulic turbine serves to recover the kinetic energy of the water flow that leaves the runner. The amount of this energy depends on the turbine type, its specific speed and flow capacity. For high specific speed turbines, influence of the draft tube on the turbine efficiency is more essential. The height of the elbow-type draft tube has a considerable influence on its performance. Statistics show that increasing the height of the draft tube leads to a considerable increase of the turbine efficiency, but it also increases the scope and cost of civil works. The present paper presents the solution of the multi-objective optimization problem directed to minimization of the height of the toroidal elbow-type draft tube and maximization of the overall efficiency of the turbine. During optimization, 11-19 parameters that determine the shape and dimensions of the draft tube were subject to variation. In optimization, efficiency was maximized in two operating points: best efficiency point and full-load point. Cavitation qualities were defined as constraints. In order to correctly assess the turbine efficiency and cavitation, the computational domain included one channel of the distributor, one channel of the runner and draft tube; whereas losses in the spiral case and stay ring were determined using empiric formulae. Within the optimization the flow analysis was performed using 3D steady-state Reynolds averaged Navier Stokes equations closed by the k-ε turbulence model. On solid walls the method of wall functions was used. The turbine head, being the difference of specific energies in the inlet and outlet cross sections, was pre-set as a constant value, while the discharge was determined in the course of solution of the problem. A multi-objective genetic algorithm was used to solve the optimization problem. For the draft tube shapes obtained in the course of optimization, their performance characteristics and flow character were compared by means of CFD. It was found that it is possible to significantly reduce the height of the classical toroidal draft tubes without considerably affecting their performance characteristics.

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Section: Introductionmentioning

confidence: 99%

“…1). CADRUN-opt optimization tool developed earlier by the authors is presently used in Hydraulic Machine Division, Power Machines, for automatic design of runners for Francis [1] and Kaplan [2] turbines. So the present paper is dedicated to extend the capabilities of CADRUN-opt tool to automatic design of the draft tubes.…”

Section: Introductionmentioning

confidence: 99%

Optimization design of the elbow draft tube of the hydraulic turbine

Semenova¹,

Чирков

Skorospelov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Several examples of Francis turbine runner optimization exist (Enomoto, Kurosawa, & Kawajiri, 2012;Nakamura & Kurosawa, 2009;Pilev et al, 2012), some even optimizing the runner and draft tube simultaneously (Lyutov, Chirkov, Skorospelov, Turuk, & Cherny, 2015). Most of these attempts deal with medium-to-high specific speed Francis turbines, but other turbine types have also been optimized using similar techniques (Ezhilsabareesh, Rhee, & Samad, 2018;Semenova et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimization procedure for variable speed turbine design

Tengs

Storli

Holst

2018

Engineering Applications of Computational Fluid Mechanics

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This article outlines a design procedure for variable speed Francis turbines using optimization software. A fully parameterized turbine design procedure is implemented in MATLAB . ANSYS CFX is used to create hill diagrams for each turbine design. An operation mode of no incidence losses is chosen, and the mean efficiency in the range ±20% of the best efficiency point is used as optimization criterion. This characteristic is extracted for each design, and optiSLang is used for system coupling and optimization. In the global optimization loop, the downhill simplex method is used to maximize the turbine performance. For this article, the bounding geometry of the runner is kept as in the original configuration. This way, the performance of the different variable speed turbines can be compared directly. Two optimization parameters describing the blade leading-edge geometry have been used in the optimization procedure. The resulting design was an almost circular leading edge, and shows an increase in mean efficiency of 0.25% compared to the reference case. There was a significant change in the turbine performance, with close to no change at the best efficiency point, and an increase in efficiency of almost 1% at low rotational speed. The outlined procedure is parallelizable and can be performed within an industrial timeframe. ARTICLE HISTORY

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“…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.…”

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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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Multiobjective optimal design of runner blade using efficiency and draft tube pulsation criteria

Cited by 7 publications

References 1 publication

Optimization design of the elbow draft tube of the hydraulic turbine

Optimization design of the elbow draft tube of the hydraulic turbine

Optimization procedure for variable speed turbine design

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

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