In aerodynamic design, accurate and robust surrogate models are important to accelerate computationally expensive computational fluid dynamics (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 threefold: (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 multipoint optimization of the transonic NASA rotor 37, yielding increased compressor efficiency in less than 48 h on 100 central processing unit cores. The optimized rotor geometry features precompression 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 Precompression
Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received September 21, 2018; final manuscript received October 17, 2018; published online January 25, 2019. Editor: Kenneth Hall.
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Joly, M., Sarkar, S., and Mehta, D. (January 25, 2019). "Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression." ASME. J. Turbomach. May 2019; 141(5): 051011. https://doi.org/10.1115/1.4041808
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