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TECHNICAL PAPERS

An Improved Neural-Network-Based Calibration Method for Aerodynamic Pressure Probes

[+] Author and Article Information
Hui-Yuan Fan

Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, HKSAR, P. R. ChinaSER Turbomachinery Research Center, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, P. R. China

Wei-zhen Lu

Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, HKSAR, P. R. Chinae-mail: bcwzlu@city.edu.hk

Guang Xi, Shang-jin Wang

SER Turbomachinery Research Center, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, P. R. China

J. Fluids Eng 125(1), 113-120 (Jan 22, 2003) (8 pages) doi:10.1115/1.1523063 History: Received January 06, 2001; Revised July 29, 2002; Online January 22, 2003
Copyright © 2003 by ASME
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References

Figures

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Configurations of the five-hole pressure probes
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(a) Simplified experimental scheme, (b) probe coordinate system
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Structures of neural networks: (a) for the semiflow forward-facing measurement case, (b) for the nonflow forward-facing measurement case
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Test results of neural-network-based method to calibrate five-hole probe for semiflow forward-facing measurement; (a) the target parameters and their predictions, (b) column-error plots of the testing results
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Test results of neural-network-based method to calibrate five-hole probe for nonflow forward-facing case; (a) target parameters and their predictions, (b) column error plots of testing results
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Performance comparison of iDE, oDE, and BP algorithm for neural network training

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