Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary AlgorithmsReport as inadecuate

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Mathematical Problems in EngineeringVolume 2012 2012, Article ID 752931, 22 pages

Research Article

Department of Mechanical and Aerospace Engineering RECAPT, Gyeongsang National University, 900 Gajwa-dong, Gyeongnam, Jinju 660-701, Republic of Korea

Department of Eco-Machinery, Korea Institute of Machinery and Materials, 171 Jang-dong, Daejeon 305-343, Republic of Korea

Received 29 May 2012; Revised 7 September 2012; Accepted 7 September 2012

Academic Editor: Gerhard-Wilhelm Weber

Copyright © 2012 Soo-Yong Cho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network ANN was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics CFD and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation.

Author: Soo-Yong Cho, Kook-Young Ahn, Young-Duk Lee, and Young-Cheol Kim

Source: https://www.hindawi.com/


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