Multi-objective shape optimization of a hydraulic turbine runner using efficiency, strength and weight criteria

Чирков, Д. В.; Ankudinova, Anna S.; Kryukov, A. E.; Cherny, S. G.; Skorospelov, V. A.

doi:10.1007/s00158-018-1914-6

Cited by 27 publications

(10 citation statements)

References 17 publications

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“…While the given field is interdisciplinary, from the mathematical point of view one would not only like to propose and analyze new optimization algorithms, but also to understand the existence and the properties of optimal solutions. While for mono-criteria optimization such a framework has been established [3,17,20,26,34], a general framework for multi-criteria optimization is still missing; see however [18,21,34] for numerical studies addressing the topic.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization

Gottschalk

Reese

2021

J Optim Theory Appl

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A simple multi-physical system for the potential flow of a fluid through a shroud, in which a mechanical component, like a turbine vane, is placed, is modeled mathematically. We then consider a multi-criteria shape optimization problem, where the shape of the component is allowed to vary under a certain set of second-order Hölder continuous differentiable transformations of a baseline shape with boundary of the same continuity class. As objective functions, we consider a simple loss model for the fluid dynamical efficiency and the probability of failure of the component due to repeated application of loads that stem from the fluid’s static pressure. For this multi-physical system, it is shown that, under certain conditions, the Pareto front is maximal in the sense that the Pareto front of the feasible set coincides with the Pareto front of its closure. We also show that the set of all optimal forms with respect to scalarization techniques deforms continuously (in the Hausdorff metric) with respect to preference parameters.

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Section: Introductionmentioning

confidence: 99%

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization

Gottschalk

Reese

2021

J Optim Theory Appl

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“…For a general introduction into the field of multiobjective optimization we refer to Miettinen (1998) and Ehrgott (2005). Problems of multiobjective shape optimization have mainly been approached with evolutionary multiobjective optimization (EMO) algorithms, see, for example, Deb and Goel (2002); Chirkov et al (2018). However, recently multiobjective PDE constrained optimization problems have also been considered from an optimal control point of view in Iapichino et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Hypervolume scalarization for shape optimization to improve reliability and cost of ceramic components

et al. 2021

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In engineering applications one often has to trade-off among several objectives as, for example, the mechanical stability of a component, its efficiency, its weight and its cost. We consider a biobjective shape optimization problem maximizing the mechanical stability of a ceramic component under tensile load while minimizing its volume. Stability is thereby modeled using a Weibull-type formulation of the probability of failure under external loads. The PDE formulation of the mechanical state equation is discretized by a finite element method on a regular grid. To solve the discretized biobjective shape optimization problem we suggest a hypervolume scalarization, with which also unsupported efficient solutions can be determined without adding constraints to the problem formulation. FurthIn this section, general properties of the hypervolumeermore, maximizing the dominated hypervolume supports the decision maker in identifying compromise solutions. We investigate the relation of the hypervolume scalarization to the weighted sum scalarization and to direct multiobjective descent methods. Since gradient information can be efficiently obtained by solving the adjoint equation, the scalarized problem can be solved by a gradient ascent algorithm. We evaluate our approach on a 2 D test case representing a straight joint under tensile load.

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“…Multiobjective evolutionary algorithms are widely used in runner optimization design systems [21][22][23]. The optimization strategy usually consists of a runner design method, design of experiment (DOE), CFD analysis, response surface methodology, and multiobjective genetic algorithm method.…”

Section: Introductionmentioning

confidence: 99%

Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

Huang

Zhu

2020

Processes

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The application of a cryogenic liquefied natural gas expander can reduce the production of flash steam and improve the efficiency of natural gas liquefaction. Like traditional hydraulic machinery, cavitation will occur during the operation of a liquefied natural gas expander, in particular, there is a strong vortex flow in the draft tube, and the cavitation phenomenon is serious. In this paper, the energy loss coefficient of the draft tube is used to describe the cavitation flow in the draft tube, and the goal of reducing the cavitation in the draft tube is achieved through the optimization design of the runner. Different runner models within the range of design parameters were obtained using the Latin hypercube test, and the relationship between design parameters and objective functions is constructed by a second-order response surface model. Finally, the optimized runners were obtained using a genetic algorithm. The effects of blade loading distribution and blade lean angles on the cavitation in the draft tube were studied. According to the optimization results, the blade loading distribution and blade lean angles are recommended in the end.

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Multi-objective shape optimization of a hydraulic turbine runner using efficiency, strength and weight criteria

Cited by 27 publications

References 17 publications

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization

Hypervolume scalarization for shape optimization to improve reliability and cost of ceramic components

Cavitating Flow Suppression in the Draft Tube of a Cryogenic Turbine Expander through Runner Optimization

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