Governed by: Ministry of Industry and Information Technology of the People's Republic of China
Sponsored by: Northwestern Polytechnical University  Chinese Society Aeronautics and Astronautics
Address: Aviation Building,Youyi Campus, Northwestern Polytechnical University
Tandem Cascade Flow Prediction by POD-RBFN Reduced Order Model
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Affiliation:

Northwestern Polytechnical University

Clc Number:

V231.3

Fund Project:

Taicang City Innovation Leading Special Project (TC2019DYDS09)

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    Abstract:

    The cost of obtaining a large amount of flow field structure by traditional experiment or CFD is unacceptable, so it is significance to develop faster forecasting calculation methods. Proper Orthogonal Decomposition (POD) was used in this paper to extract the dominant mode of the tandem flow field. Radial Basis Function Network (RBFN) is used to respond to the coefficients of the POD basis functions to realize the construction of the reduced-order prediction model of the flow field. Then the adaptive sampling method was developed for the reduced order model. The prediction model is verified by the unsteady flow field data of a cascade cascade. It is concluded that the hybrid method can be utilized to accurately predict the aerodynamics parameters and flow field of tandem cascade. Compared with static sampling, the number of samples required for adaptive sampling to achieve the same reconstruction accuracy is reduced by about 25%.

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SHANG Xun, LIU Hanru, DU Yican, HU Zhijie. Tandem Cascade Flow Prediction by POD-RBFN Reduced Order Model[J]. Advances in Aeronautical Science and Engineering,2022,13(5):86-94

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History
  • Received:October 11,2021
  • Revised:January 22,2022
  • Adopted:February 28,2022
  • Online: July 25,2022
  • Published: