In this work we develop an efficient approach for computationally expensive multiobjective design optimization problems. In this approach we bring together design of experiment, a response surface model, a genetic algorithm, and computational-fluid-dynamics analysis tools to provide an integrated optimization system. We use an improved hypercube sampling to preselect an array of design points on which the computational-fluid-dynamics code will run. Then a computationally cheap surrogate model is constructed based on response surface approximation. A real-coded genetic algorithm is then applied on the surrogate model to perform multiobjective optimization. Representative solutions are chosen from the Pareto-optimal front to verify against the computational-fluid-dynamics . code. This proposed method is used in the redesign of a single-stage turbopump, a two-stage turbopump, and the NASA rotor67 transonic compressor blade. For the single-stage pump optimization problem, we can improve the total head rise by 1.2% with the same power input; for the multistage pump problem, we can improve the total head rise by 0.5% at the same power input; for the rotor67 compressor blade design, we can increase the pressure ratio by 1.8% or reduce the entropy generation by 6.2%. We achieve these with a much reduced computational cost.
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