International Journal of Fluid Machinery and Systems

Korean Society for Fluid machinery (한국유체기계학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Multi-Objective Optimization of Impeller for High-Power Centrifugal Compressor

  • Kang, Hyun-Su (Graduate School of Mechanical Engineering, Sungkyunkwan University) ;
  • Kim, Youn-Jea (School of Mechanical Engineering, Sungkyunkwan University)
  • Received : 2015.10.29
  • Accepted : 2015.11.08
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a method for the multi-objective optimization of an impeller for a centrifugal compressor using fluid-structure interaction (FSI) and response surface method (RSM) was proposed. Numerical simulation was conducted using ANSYS CFX and Mechanical with various configurations of impeller geometry. Each design parameter was divided into 3 levels. A total of 15 design points were planned using Box-Behnken design, which is one of the design of experiment (DOE) techniques. Response surfaces based on the results of the DOE were used to find the optimal shape of the impeller. Two objective functions, isentropic efficiency and equivalent stress were selected. Each objective function is an important factor of aerodynamic performance and structural safety. The entire process of optimization was conducted using the ANSYS Design Xplorer (DX). The trade-off between the two objectives was analyzed in the light of Pareto-optimal solutions. Through the optimization, the structural safety and aerodynamic performance of the centrifugal compressor were increased.

Keywords

References

  1. A. H. Lerche, J. J. Moore, N. M. White and J. Hardin, "Dynamic stress prediction in centrifugal compressor blades using fluid structure interaction," Proceeding of ASME Turbo Expo, Vol. 6, pp. 193-200, 2012.
  2. Park, T. G., Jung, H. T., Kim, H. B. and Park, J. Y., 2011, "Numerical study on the aerodynamic performance of the turbo blower using fluid-structure interaction method," Journal of the Korea Society for Power System Engineering, Vol. 15, No. 6, pp. 35-40 (in korean). https://doi.org/10.9726/kspse.2011.15.6.035
  3. J. H. Kim, J. H. Choi, A. Husain and K. Y. Kim, "Multi-objective optimization of a centrifugal compressor impeller through evolutionary algorithms," J. Power and Energy, Vol. 224, pp. 711-721, 2010. https://doi.org/10.1243/09576509JPE884
  4. J. H. Kim, J. H. Choi and K. Y. Kim, "Design optimization of a centrifugal compressor impeller using radial basis neural network method," Proceeding of ASME Turbo Expo, Vol. 7, pp. 443-451, 2009.
  5. Benni, E. and Pediroda, V., "Aerodynamic optimization of an industrial centrifugal compressor impeller using genetic algorithm," Proceeding of Eruogen, pp. 467-472, 2001.
  6. X. F. Wang, G. Xi and Z. H. Wang, "Aerodynamic optimization design of centrifugal compressor's impeller with Kriging model," J. Power and Energy, Vol. 220, pp. 589-597, 2006. https://doi.org/10.1243/09576509JPE201
  7. S. M. Kim, J. Y. Park, K. Y. Ahn, and J. H. Baek, "Numerical investigation and validation of the optimization of a centrifugal compressor using a response surface method," J. Power and Energy, Vol. 224, pp. 251-259, 2009.
  8. D. Bonaiuti, A. Arnon, M. Ermini, and L. Baldassarre, "Analysis and optimization of transonic centrifugal compressor impellers using the design of experiments technique," ASME J. Turbomach., Vol. 128, No. 4, pp. 786-797, 2006. https://doi.org/10.1115/1.1579507
  9. J. E. Bardina, P. G. Huang, and T. Coakley, "Turbulence modeling validation," 28th AIAA Fluid Dynamics Conference, AIAA-1997-2121, 1997.
  10. P. J. Greem and B. E. Silverman, "Nonparametric regression and generalized linear models," New York: Chapman & Hall, 1994.
  11. B. H. Ju, T. M. Cho, D. H. Jung and B. C. Lee, "An error assessment of the Kriging based approximation model using a mean square error," Trans. Korean Soc. Mech. Eng. A, Vol. 30, No. 8, pp. 923-930, 2006 (in korean). https://doi.org/10.3795/KSME-A.2006.30.8.923