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research-article

Machine Learning enabled Adaptive Optimization of a Transonic Compressor Rotor with pre-compression

[+] Author and Article Information
Michael Joly

Thermal & Fluid Sciences, United Technologies Research Center, East Hartford, CT, USA
jolymm@utrc.utc.com

Soumalya Sarkar

Autonomous and Intelligent Systems, United Technologies Research Center, East Hartford, CT, USA
sarkars@utrc.utc.com

Dhagash Mehta

Autonomous and Intelligent Systems, United Technologies Research Center, East Hartford, CT, USA
mehtadb@utrc.utc.com

1Corresponding author.

ASME doi:10.1115/1.4041808 History: Received September 21, 2018; Revised October 17, 2018

Abstract

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.

Copyright (c) 2018 by ASME
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