Study on artificial neural network modeling and wind tunnel test for the FADS system applied to the vehicle with sharp nosed fore-bodies

Wang Peng; Jin Xin

doi:10.11729/syltlx20180125

Volume 33 Issue 5

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Wang Peng, Jin Xin. Study on artificial neural network modeling and wind tunnel test for the FADS system applied to the vehicle with sharp nosed fore-bodies[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(5): 57-63. doi: 10.11729/syltlx20180125

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Study on artificial neural network modeling and wind tunnel test for the FADS system applied to the vehicle with sharp nosed fore-bodies

doi: 10.11729/syltlx20180125

Wang Peng^,,
Jin Xin

China Academy of Aerospace Aerodynamics, Beijing 100074, China

Received Date: 2018-09-11
Rev Recd Date: 2019-06-13
Publish Date: 2019-10-25

Abstract

Abstract

The solving model and its accuracy for the Flush Air Data Sensing (FADS) system applied to the vehicle with sharp nosed fore-bodies are studied. Firstly, the pressure ports configuration for sharp nosed fore-bodies is determined based on the theoretical model of the FADS system applied to the blunt fore-bodies. Secondly, a typical wind tunnel test for the FADS pressure ports is implemented, and CFD results and wind tunnel test are analyzed systematically. Finally, the solving model and algorithm are developed for the FADS system based on the Radial Basis Function (RBF) neural network modeling, and the neural network model solving accuracy for the FADS system is analyzed. The results show that, the solving accuracy of the RBF neural network model for the FADS system is high enough. The neural network outputs agree well with the wind tunnel test data, and the testing error distributions (absolute error distribution) for the angle of attack, angle of sideslip, free stream static pressure and Mach number are less than 0.1°, 0.1°, 50.0 Pa and 0.01, respectively. The results also present that the artificial neural network modeling for the FADS system can be further developed in the future.
- Flush Air Data Sensing (FADS) system,
- sharp nosed,
- artificial neural network,
- wind tunnel test,
- accuracy

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References(17)

References

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