Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles

Hongzhi Cheng, Ziliang Li, Penghao Duan, Xingen Lu, Shengfeng Zhao and Yanfeng Zhang

Applied Energy, 2023, vol. 352, issue C, No S0306261923013363

Abstract: Axial compressors are susceptible to uncertainties during their manufacturing and operation, resulting in reduced efficiency and performance dispersion. However, uncertainty quantification and robust design of compressors remains challenging due to the complexity of structure and internal flow. In this study, an automated framework for uncertainty quantification and robustness optimization of micro axial compressors is proposed. Ten geometrical uncertainties are propagated for the nominal design point and two off-design points, i.e., near stall and choke conditions, respectively. The main objective of this paper is to optimize the aerodynamic robustness performance at these operating points. The sparse grid-based probabilistic collocation method is used to propagate these uncertainties, and a multi-objective genetic algorithm is employed to perform robust optimization based on a novel constructed surrogate model.

Keywords: Micro transonic compressor; Geometric uncertainties; Uncertainty quantification; Surrogate model; Aerodynamic robustness optimization; Multiple working points (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013363

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DOI: 10.1016/j.apenergy.2023.121972

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