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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Joly, Michael Sarkar, Soumalya Mehta, Dhagash |
| Copyright Year | 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. |
| Sponsorship | International Gas Turbine Institute |
| File Format | |
| ISBN | 9780791851012 |
| DOI | 10.1115/GT2018-77098 |
| Volume Number | Volume 2C: Turbomachinery |
| Conference Proceedings | ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition |
| Language | English |
| Publisher Date | 2018-06-11 |
| Publisher Place | Oslo, Norway |
| Access Restriction | Subscribed |
| Subject Keyword | Performance evaluation Compression Uncertainty Stability Computational fluid dynamics Nasa Rotors Compressors Optimization Design Geometry Machine learning Shock (mechanics) Failure |
| Content Type | Text |
| Resource Type | Article |
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