Optimization Strategy for an Axial-Flow Compressor Using a Region-Segmentation Combining Surrogate Model
Publication: Journal of Aerospace Engineering
Volume 31, Issue 5
Abstract
Axial-flow compressors work against varying inlet boundary layers in real working conditions and are therefore required to perform well and robustly. This paper presents a surrogate-based optimization procedure applied to a transonic compressor to improve its efficiency and reduce the sensitivity of efficiency variation to uncertain inlet boundary layer thicknesses while maintaining the total pressure ratio. The aerodynamic optimization of compressors involves high-fidelity computational models that would cost high amounts of computational time. To implement the optimization, a region-segmentation combining surrogate model is used that is based on combinational use of the region-segmentation idea and combining surrogate modeling method to further improve prediction accuracy and reduce computational cost. Based on the region-segmentation combining surrogate model, an optimization procedure is constructed and applied to a transonic compressor. The computational results of the benchmark function and compressor optimization indicate the validity of the region-segmentation combining surrogate model in improving the prediction accuracy and computational efficiency. The optimization procedure also presents the ability to improve the compressor efficiency and make the compressor perform well and robustly at uncertain inlet boundary layer thicknesses while maintaining the total pressure ratio. The achieved aerodynamic benefits of the compressor have demonstrated the feasibility and effectiveness of the optimization strategy.
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Acknowledgments
The authors would like to gratefully acknowledge the support by the National Natural Science Foundation of China (Nos. 51636001 and 51706008), China Postdoctoral Science Foundation (No. 2017M610742), Aeronautics Power Foundation (No. 6141B090315), the support by the Academic Excellence Foundation of BUAA for Ph.D. Students, the support by the Innovation Practice Foundation of BUAA for Graduates (YCSJ-01-2016-03), and the support by the China Scholarship Council (CSC) for joint Ph.D. students.
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Information & Authors
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Published In
Journal of Aerospace Engineering
Volume 31 • Issue 5 • September 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jul 31, 2017
Accepted: Apr 3, 2018
Published online: Jun 28, 2018
Published in print: Sep 1, 2018
Discussion open until: Nov 28, 2018
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