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The application of support vector regression and virtual sample generation technique in the optimization design of transonic compressor

机译：The application of support vector regression and virtual sample generation technique in the optimization design of transonic compressor

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摘要

With the development of artificial intelligence, machine learning technique has been applied in turbomachinery optimization. However, the performance of a classic machine learning method largely depends on the quantity and quality of data samples. Due to the high cost of computational fluid dynamics models, a small sample size may restrict the reliability of machine learning models and optimization results. To tackle the small sample size problem in the turbomachinery optimization, this study uses support vector regression as a surrogate model. As a development of statistic learning theory, it performs well in the case of small samples. In order to expand the sample set, a mega-trend diffusion technique is adopted to generate virtual samples. The virtual samples generated from real samples can fill in the information gaps in the sparse domain. Then the support vector regression model is updated iteratively by virtual samples instead of real samples during optimization. In this way the calls of time-consuming numerical models are reduced and the optimization process is accelerated. The proposed method is validated by a high-dimensional aerodynamic optimization on the transonic compressor NASA Rotor 37. Firstly, a multi-objective optimization based on Free-form Deformation parameterization, support vector regression and NSGA-II algorithm is carried out. The optimized isentropic efficiency and total pressure ratio are increased by 1.7 and 12, respectively. The choked mass flow rate is also raised. Then the virtual samples are generated using mega-trend diffusion and the surrogate model is updated. Finally, the optimization with virtual sample generation increases the efficiency by 2.3 compared to the baseline. Consequently, the proposed method can improve optimization results and relieve computational burden.

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