Paper Title:
Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network
  Abstract

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

  Info
Periodical
Chapter
Chapter 1: Applied Mechanics Mechanical Engineering
Edited by
Honghua Tan
Pages
534-539
DOI
10.4028/www.scientific.net/AMM.138-139.534
Citation
L. H. Chen, Q. Z. Yang, J. H. Cui, "Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network", Applied Mechanics and Materials, Vols. 138-139, pp. 534-539, 2012
Online since
November 2011
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$32.00
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