Centrifugal compressor blade optimization based on uniform design and genetic algorithms

Abstract

An optimization approach to centrifugal compressor blade design, incorporating uniform design method (UDM), computational fluid dynamics (CFD) analysis technique, regression analysis method and genetic algorithms (GA), is presented. UDM is employed to generate the geometric information of trial samples whose performance is evaluated by CFD technique. Then, function approximation of sample information is performed by regression analysis method. Finally, global optimization of the approximative function is obtained by genetic algorithms. Taking maximum isentropic efficiency as objective function, this optimization approach has been applied to the optimum design of a certain centrifugal compressor blades. The results, compared with those of the original one, show that isentropic efficiency of the optimized impeller has been improved which indicates the effectiveness of the proposed optimization approach.

References

1. Burguburu S, Toussaint C, Bonhomme C, et al.. Numerical optimization of turbomachinery bladings. ASME Journal of Turbomachinery, 2004, 126(1): 91–100. doi:10.1115/1.1645869
2. Arensa K, Rentropa P, Stollb S O, et al.. An adjoint approach to optimal design of turbineblades. Applied Numerical Mathematics, 2005, 53(2): 93–105. doi:10.1016/j.apnum.2004.11.003
3. Wang X J, Damodaran M . Aerodynamic Shape optimizationusing computational fluid dynamics and parallel simulated annealingalgorithms. AIAA Paper 2000 –4847
4. Pierret S, Van den Braembussche R A . Turbomachineryblade design using a Navier-Stokes solver and artificial neural network. ASME Journal of Turbomachinery, 1999, 121(4): 326–332
5. Simpon T W, Peplinski J, Koch P N, et al.. On the use of statistics in design and the implicationsfor deterministic computer experiments. Proceedings of DETC'97. California,USA, 1997 : 1–10
6. Zangeneh M . Invsicid/viscousinteraction method for 3D inverse design of centrifugal impellers. ASME Journal of Turbomachinery, 1994, 116(2), 280–290. doi:10.1115/1.2928362
7. Trigg M A, Tubby G R, Sheard A G . Automatic genetic optimization approach to two dimensionalblade profile design for steam turbines. ASME Journal of Turbomachinery, 1999, 121(1): 11–17
8. Vanderplaats G N . Numerical optimization techniques for engineering design: with applications. New York: McGraw-Hill, 1984
9. Fang K T, Wang Y . Number-Theoretic Methodsin Statistics. London: Chapman and Hall, 1994
10. Shu X W, Gu C G, Wang T, et al.. Aerodynamic Optimization for Turbine Blade Basedon Hierarchical Fair Competition Genetic Algorithms with Dynamic Niche. Chinese Journal of Mechanical Engineering, 2007, 20(6): 38–42