A Novel Calibration Method for Multi-Hole Pitot Tubes

Abstract:

The ANFIS algorithm was first applied to calibration modeling of the multi-hole pitot tube in this study. Owing to its capability of efficient learning, easy implementation and excellent explanation through fuzzy rules, ANFIS can help identify the dominant parameters and construct fuzzy learning system. After determination of the ANFIS structure from the calibration data, the network of pitot tube calibration parameters was established and the correlation among non-dimensional pressure coefficients, flow angle and flow velocity were constructed as well. Meanwhile, the air velocity can be predicted based on the measurements of flow angles and flow angle coefficients. It can reach to a high consistency of 0.0068 with the original data after iteration. Eventually, ANFIS can be integrated with real-time data acquisition system and wind tunnel due to its programmability. A large database consisting of flow properties, flow angles and the non-dimensional pressure coefficients can be efficiently established and will be helpful for shortening the calibration procedures.

• Periodical: Applied Mechanics and Materials (Volumes 764-765)
• Pages: 1329-1333
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.764-765.1329
• Online since: May 2015
• Authors: Hsin Hung Lee*
• Keywords: ANFIS, Calibration, Pitot Tube, Smokestack

References

[1] O.K. Rediniotis, G. Chrysanthakopoulos: Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe, ASME J. Fluids Eng., Vol. 120 (1998), pp.95-101.

DOI: 10.1115/1.2819670

[2] O.K. Rediniotis, R. Vijayagopal: Miniature Multihole Pressure Probes and Their Neural-Network-Based Calibration, AIAA J., Vol. 37 (1999), pp.666-674.

DOI: 10.2514/3.14225

[3] H.Y. Fan, W. Lu, G. Xi: An Improved Neural-Network-Based Calibration Method for Aerodynamic Pressure Probes, ASME J. Fluids Eng., Vol. 125 (2003), pp.113-120.

DOI: 10.1115/1.1523063

[4] R. Babuška: Neuro-Fuzzy Methods for Modeling and Identification, Recent Advances in Intelligent Paradigms and Applications, Springer-Verlag, Heidelberg (2002).

[5] J.S. R Jang: ANFIS: Adaptive-Network-based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, Vol. 23 (1993), pp.665-685.

DOI: 10.1109/21.256541

[6] T. Takagi, M. Sugeno: Derivation of Fuzzy Control Rules from Human Operator's Control Actions, Proceeding of IFAC Symposium Fuzzy Information, Knowledge Representation and Decision Analysis, Marseilles, France, (1983) pp.55-60.

DOI: 10.1016/s1474-6670(17)62005-6

[7] T. Takagi, M. Sugeno: Fuzzy Identification of Systems and Its Application to Modeling and Control, IEEE Trans. Syst., Man Cybern., Vol. 15 (1985), pp.116-132.

DOI: 10.1109/tsmc.1985.6313399

[8] J.S. R Jang, C. T Sun: Neuro-Fuzzy Modeling and Control, Proceedings of the IEEE, Vol. 83 (1995), pp.378-406.

[9] J.S. R Jang, C. T Sun, E. Mizutani: Neuro-Fuzzy and soft computing, Prentice Hall: Englewood Cliffs, New Jersey (1997).

[10] Fern, O D. Ramírez-Figueroa, Pedro Ponce-Cruz, Intelligent Control Systems with LabVIEW, Springer (2010).