2005
DOI: 10.2514/1.9484
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Application of Simultaneous Perturbation Stochastic Approximation Method for Aerodynamic Shape Design Optimization

Abstract: Aerodynamic shape design optimization problems, such as inverse and constrained airfoil design and axisymmetric nozzle design, are investigated by applying the simultaneous perturbation stochastic approximation (SPSA) method to objective functions that are estimated during each design iteration using a finite volume computational fluid dynamics technique for solving the compressible Navier-Stokes equations. The SPSA method has been demonstrated in the literature as having significant advantages over stochastic… Show more

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Cited by 20 publications
(7 citation statements)
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References 31 publications
(28 reference statements)
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“…The performance parameters are parameterized from evaluation results of flow solvers, and should be easily evaluated by designers. The chosen performance parameters can be efficiency [1,2], total pressure ratio [5], losses [5,6], velocity distribution [7,8], pressure distribution [9], and loading [2,10]. The 3D CFD flow solvers could be applicable for calculating the compressor performances.…”
Section: Parameterizationmentioning
confidence: 99%
“…The performance parameters are parameterized from evaluation results of flow solvers, and should be easily evaluated by designers. The chosen performance parameters can be efficiency [1,2], total pressure ratio [5], losses [5,6], velocity distribution [7,8], pressure distribution [9], and loading [2,10]. The 3D CFD flow solvers could be applicable for calculating the compressor performances.…”
Section: Parameterizationmentioning
confidence: 99%
“…Hutchison and Spall (38) give a criterion for stopping stochastic approximation. Some of the SPSA applications in different fields are given elsewhere (39)(40)(41). The SPSA has also been used in neural network applications.…”
Section: Performance Evaluation Eren Erman Ozguven and Kaan Ozbaymentioning
confidence: 99%
“…Traditionally, investigators have used two families of optimization techniques, namely stochastic and deterministic methods in aerodynamic design optimization. Examples of stochastic methods include genetic algorithms (GAs) and simulated annealing (SA) algorithms, which are capable of determining global minimum points and are easy to implement in well-established CFD codes [50]. However, these approaches tend to be computationally expensive as they generally require a very large number of CFD computations.…”
Section: Introductionmentioning
confidence: 99%