RBF Neural Network Based Prediction on Blade Surface Pressure Fields in Compressors

YAO Minghui; WANG Xingzhi; WU Qiliang; NIU Yan

doi:10.21656/1000-0887.440054

Volume 44 Issue 10

Oct. 2023

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Article Navigation > Applied Mathematics and Mechanics > 2023 > 44(10): 1187-1199

YAO Minghui, WANG Xingzhi, WU Qiliang, NIU Yan. RBF Neural Network Based Prediction on Blade Surface Pressure Fields in Compressors[J]. Applied Mathematics and Mechanics, 2023, 44(10): 1187-1199. doi: 10.21656/1000-0887.440054

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RBF Neural Network Based Prediction on Blade Surface Pressure Fields in Compressors

doi: 10.21656/1000-0887.440054

1.
School of Aeronautics and Astronautics, Tiangong University, Tianjin 300387, P.R.China
2.
School of Control Science and Engineering, Tiangong University, Tianjin 300387, P.R.China

Received Date: 2023-03-02
Rev Recd Date: 2023-05-10
Publish Date: 2023-10-31

Abstract

Abstract

The airflow characteristics of the internal flow path of an aero-engine compressor are complex, and the vortex flow field around the blade is characterized by high pressure, high speed, rotation, and unsteadiness. Therefore, there is an urgent need to calculate and predict the aerodynamic characteristics of the complex flow field around the compressor blade efficiently and accurately. The computational fluid dynamics (CFD) method was used to generate the aerodynamic load distribution on the blade surface under different operating conditions for the study of the complex flow fields around aero-engine blades. The radial based function (RBF) neural network was applied to establish the pressure surface aerodynamic load prediction model, and the neural network modeling method was combined with the flow field calculation. The neural network method can learn and train the CFD-based data set to properly compensate the errors from the CFD, which provides a reference for the effective prediction of the complex flow fields around aero-engine compressor blades.
- radial basis neural network,
- computational fluid dynamics,
- compressor blade flow field

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References(26)

References

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