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2018
DOI: 10.1016/j.asoc.2017.11.048
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Efficiency of bio- and socio-inspired optimization algorithms for axial turbomachinery design

Abstract: Turbomachinery design is a complex problem which requires a lot of experience. The procedure may be speed up by the development of new numerical tools and optimization techniques. The latter rely on the parameterization of the geometry, a model to assess the performance of a given geometry and the definition of an objective functions and constraints to compare solutions. In order to improve the reference machine performance, two formulations including the off-design have been developed. The first one is the ma… Show more

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Cited by 7 publications
(5 citation statements)
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“…MFT was employed to design conventional and CR axial fans by Nouri et al [9,10] and Wang et al [11,12]. It was also coupled with optimization techniques to improve the design of axial pumps by Ait chick et al [24,25].…”
Section: Design Methods Of the Innovative Crcc Configurationmentioning
confidence: 99%
“…MFT was employed to design conventional and CR axial fans by Nouri et al [9,10] and Wang et al [11,12]. It was also coupled with optimization techniques to improve the design of axial pumps by Ait chick et al [24,25].…”
Section: Design Methods Of the Innovative Crcc Configurationmentioning
confidence: 99%
“…Due to its simplicity and efficiency, Particle Swarm Optimization (PSO) is suitable for this kind of optimization problem. PSO is a metaheuristic non-intrusive algorithm that is widely used in engineering, including the turbomachinery domain [28][29][30][31]. In 1995, Eberhart and Kennedy [32] attempted to develop an algorithm that describes the swarm motion of animals, such as bees, birds, or even fishes.…”
Section: Efficiency Investigationmentioning
confidence: 99%
“…As presented in Ait Chikh et al, 20 two methods are commonly used for designing turbomachines: the inverse method for the design of new machine, and the direct method for analyzing the performance of an existing machine.…”
Section: Design Model Of An Axial Pumpmentioning
confidence: 99%