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Research Papers

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

[+] Author and Article Information
Michael Joly

Thermal and Fluid Sciences,
United Technologies Research Center,
East Hartford, CT 06108
e-mail: jolymm@utrc.utc.com

Soumalya Sarkar

Autonomous and Intelligent Systems,
United Technologies Research Center,
East Hartford, CT 06108
e-mail: sarkars@utrc.utc.com

Dhagash Mehta

Autonomous and Intelligent Systems,
United Technologies Research Center,
East Hartford, CT 06108
e-mail: mehtadb@utrc.utc.com

1Corresponding author.

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.

J. Turbomach 141(5), 051011 (Jan 25, 2019) (9 pages) Paper No: TURBO-18-1262; doi: 10.1115/1.4041808 History: Received September 21, 2018; Revised October 17, 2018

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.

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Figures

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Fig. 1

Performance of NASA rotor 37, including CFD validation, grid independency study, and baseline for optimization

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Fig. 2

Surrogate-assisted optimization

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Fig. 3

Schematic of ML4SAO framework

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Fig. 4

Receiver-operating characteristic curves of the response related to the prediction of efficiency on DOE of 150 (a) and 300 (b) candidates intended for rotor 37 optimization

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Fig. 5

Surrogate-assisted optimization on Kursawe function ((a) mean and variance of distance over ten optimizations; (b) mean and variance of spread over ten optimizations)

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Fig. 6

Surrogate-assisted optimization results on Kursawe function with most representative case of mean over ten optimization with different surrogate models ((a) Matern kernel; (b) RBF kernel GP; (c) ML4SAO); (d) Best Pareto front with 100 generations on ML4SAO)

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Fig. 7

History of single-point optimization

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Fig. 8

Performance of baseline and optima of single-point and multipoint optimizations

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Fig. 9

History of multipoint optimization

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Fig. 10

History of surrogate models selection

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Fig. 11

Turbulent kinetic energy downstream of the rotor: baseline (a) and optimized (b)

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Fig. 12

Isentropic Mach number loading at 70% span

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Fig. 13

Relative Mach number filed at 70% span: baseline (a) and optimized (b)

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