In aerodynamic design, accurate and robust surrogate models are important to accelerate computationally expensive CFD-based optimization. In this paper, a machine learning framework is presented to speed-up the design optimization of a highly-loaded transonic compressor rotor. The approach is three-fold: (1) dynamic selection and self-tuning among several surrogate models; (2) classification to anticipate failure of the performance evaluation; and (3) adaptive selection of new candidates to perform CFD evaluation for updating the surrogate, which facilitates design space exploration and reduces surrogate uncertainty. The framework is demonstrated with a multi-point optimization of the transonic NASA rotor 37, yielding increased compressor efficiency in less than 48 hours on 100 CPU cores. The optimized rotor geometry features pre-compression that relocates and attenuates the shock, without the stability penalty or undesired reacceleration usually observed in the literature.
Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Pre-Compression
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Joly, M, Sarkar, S, & Mehta, D. "Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Pre-Compression." Proceedings of the ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. Volume 2C: Turbomachinery. Oslo, Norway. June 11–15, 2018. V02CT42A050. ASME. https://doi.org/10.1115/GT2018-77098
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