Design of Compressor Impeller Using Evolutionary Optimization Technique

Abstract:

Configuration design on an impeller using to the centrifugal compressor of turbocharger was conducted to improve its performance. Impeller shape was adjusted by changing its meridional contours and blade profile. Total nine design variables were chosen with constraints. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. This ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After 7th generation, the difference of efficiency and pressure ratio predicted by ANN and CFD (Computational Fluid Dynamics) was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

• Periodical: Applied Mechanics and Materials (Volumes 271-272)
• Pages: 833-837
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.271-272.833
• Online since: December 2012
• Authors: Soo Yong Cho, Jin Han Kim, Chae Sil Kim
• Keywords: Artificial Neural Net, Compressor Impeller, Configuration Design, Design of Experiment (DOE), Genetic Algorithm (GA), Optimization

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