Skip to main content

Multi-objective optimization of an axial compressor blade

Abstract

Numerical optimization with multiple objectives is carried out for design of an axial compressor blade. Two conflicting objectives, total pressure ratio and adiabatic efficiency, are optimized with three design variables concerning sweep, lean and skew of blade stacking line. Single objective optimizations have been also performed. At the data points generated by D-optimal design, the objectives are calculated by three-dimensional Reynolds-averaged Navier-Stokes analysis. A second-order polynomial based response surface model is generated, and the optimal point is searched by sequential quadratic programming method for single objective optimization. Elitist non-dominated sorting of genetic algorithm (NSGA-II) with ε-constraint local search strategy is used for multi-objective optimization. Both objective function values are found to be improved as compared to the reference one by multi-objective optimization. The flow analysis results show the mechanism for the improvement of blade performance.

This is a preview of subscription content, access via your institution.

References

  1. S. J. Gallimore, J. J. Bolger and N. A. Cumpsty, The Use of Sweep and Diahedral in Multistage Axial Flow Compressor Blading, Part 1: University Research and Methods Development, Proceedings of ASME Turbo Expo,Amsterdam, The Netherlands, GT-2002-30328 (2002).

  2. J. D. Denton and L. Xu, The Effects of Lean and Sweep on Transonic Fan Performance, Proceedings of ASME Turbo Expo, Amsterdam, The Netherlands, GT-2002-30327 (2002).

  3. N. Cai, J. Xu and A. Benaissa, Aerodynamic and Aeroacoustic Performance of a Skewed Rotor, Proceedings of ASME Turbo Expo, Atlanta, GA, GT-2003-38592 (2003).

  4. A. Fischer, W. Riess and J. Seume, Performance of Strongly Bowed Stators in a 4-Stage High Speed Compressor, Proceedings of ASME Turbo Expo, Atlanta, GA, GT-2003-38392 (2003).

  5. E. Benini and R. Biollo, On the Aerodynamics of Swept and Leaned Transonic Compressor Rotors, ASME Turbo Expo, Spain, GT2006-90547 (2006).

  6. C. M. Jang and K. Y. Kim, Optimization of a Stator Blade using Response Surface Method in a Single-Stage Transonic Axial Compressor, Proceedings of The Institution of Mechanical Engineers, Part A — Journal of Power and Energy, 219(8) 595–603.

  7. C.-M. Jang, P. Li and K. Y. Kim, Optimization of Blade Sweep in a Transonic Axial Compressor Rotor, JSME International Journal-Series B, 48(4) (2005) 793–801.

    Article  Google Scholar 

  8. A. Samad, K. Y. Kim, T. Goel, R. T. Haftka and W. Shyy, Shape Optimization of Turbomachinery Blade using Multiple Surrogate Models, ASME Joint-U.S.-European Fluids Engineering Summer Meeting, FL, USA. FEDSM2006-98368 (2006).

  9. A. Messac, Physical programming: Effective optimization for computational design, AIAA Journal, 34(1) (1996) 149–158.

    MATH  Article  Google Scholar 

  10. R. H. Myers and D. C. Montgomery, Response Surface Methodology: Process and Product Optimization using Designed Experiments, John Wiley & Sons, New York (1995).

    Google Scholar 

  11. P. Sen and J.-B. Yang, Multiple criteria decision support in engineering design, London: Springer Verlag, New York (1998).

    Google Scholar 

  12. K. Deb, S. Agrawal, A. Pratap and T. Meyarivan, A fast and elitist multi-objective genetic algorithm for multi-objective optimization: NSGA-II, Proceedings of the parallel problem solving from nature VI conference, Paris, 849–858 (2000).

  13. V. Chankong and Y. Y. Haimes, Multiobjective decision making theory and methodology, New York: Elsevier Science (1983).

    MATH  Google Scholar 

  14. Y. Collette and P. Siarry, Multiobjective Optimization: Principles and Case Studies, New York, Springer (2003).

    Google Scholar 

  15. L. Jun, L. Guojun, F. Zhenping and L. Lijun, Multiobjective optimization approach to turbomachinery Blades Design, ASME Turbo Expo, Nevada, USA. GT2005-68303 (2005).

  16. S. Obayashi, D. Sasaki and A. Oyama, Finding Tradeoffs by using Multiobjective Optimization Algorithms, Transactions of JSASS, 47(155) (2004) 51–58.

    Google Scholar 

  17. S. Obayashi, T. Tsukahara and T. Nakamura, Multiobjective Genetic Algorithm Applied to Aerodynamic Design of Cascade Airfoils, IEEE Transactions on Industrial Electronics, 47(1) (2000) 211–216.

    Article  Google Scholar 

  18. E. Benini, Three-Dimensional Multi-Objective Design Optimization of a Transonic Compressor Rotor, Journal of Propulsion and Power, 20(3) (2004) 559–565.

    Article  Google Scholar 

  19. K. Deb and T. Goel, Multi-objective evolutionary algorithms for engineering shape design, Evolutionary Optimization (Eds. R. Sarker, M. Mohammadin, X. Yao), Kluwer, 147–176 (2000).

  20. T. Goel, R. Vaidyanathan, R. T. Haftka and W. Shyy, Response surface approximation of Paretooptimal front in multi-objective optimization, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, NY, USA. AIAA 2004-4501 (2004).

  21. L. Reid and R. D. Moore, Design and Overall Performance of Four Highly-Loaded, High-Speed Inlet Stages for an Advanced, High-Pressure-Ratio Core Compressor, NASA TP-1337 (1978).

  22. A. Jameson, W. Schmidt and E. Turkel, Numerical Solutions of the Euler Equation by Finite Volume Methods using Runge-Kutta Time Stepping Schemes, AIAA Paper No. 81-1259 (1981).

  23. B. S. Baldwin and H. Lomax, Thin Layer Approximation and Algebraic Model for Separated Turbulent Flow, AIAA Paper No. 78-257 (1978).

  24. MATLAB®, The language of technical computing, Release 14. 2004, The MathWorks Inc.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kwang-Yong Kim.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Samad, A., Kim, KY. Multi-objective optimization of an axial compressor blade. J Mech Sci Technol 22, 999–1007 (2008). https://doi.org/10.1007/s12206-008-0122-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-008-0122-5

Keywords

  • Compressor blade
  • Shape optimization
  • Genetic algorithm
  • Total pressure adiabatic efficiency