In this study, a multi-objective optimization of an axial compressor rotor blade has been performed through genetic algorithm with total pressure and adiabatic efficiency as objective functions. The non-dominated sorting of genetic algorithm-II has been implemented and confidence check has been performed at k-means clustered points among all the Pareto-optimal solutions. Reynolds-averaged Navier—Stokes equations are solved to obtain the objective function and flow field inside the compressor annulus. The objective functions are used to generate Pareto-optimal front. The design variables are selected from blade lean and thickness through the Bezier polynomial formulation. By this optimization, maximum efficiency and total pressure are increased by 1.76 and 0.41 per cent, respectively, when two extreme clustered points are considered as optimal designs.
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 -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.
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