Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-30T22:59:11.013Z Has data issue: false hasContentIssue false
  • English
  • Français

A CFD assessment of classifications for hypersonic inlet start/unstart phenomena

Published online by Cambridge University Press:  03 February 2016

J. Chang ,
D. Yu ,
W. Bao ,
Z. Xie  and
Y. Fan
J. Chang
Affiliation:
juntao_chang@yahoo.com.cn, Harbin Institute of Technology, Heilongjiang, China
D. Yu
Affiliation:
juntao_chang@yahoo.com.cn, Harbin Institute of Technology, Heilongjiang, China
W. Bao
Affiliation:
juntao_chang@yahoo.com.cn, Harbin Institute of Technology, Heilongjiang, China
Z. Xie
Affiliation:
juntao_chang@yahoo.com.cn, Harbin Institute of Technology, Heilongjiang, China
Y. Fan
Affiliation:
juntao_chang@yahoo.com.cn, Harbin Institute of Technology, Heilongjiang, China
Get access Rights & Permissions [Opens in a new window]

Abstract

Inlet start/unstart detection is one of the most important issues of hypersonic inlets and is also the foundation of protection controls of scramjets. In ground and flight tests, it is inevitably to introduce the sensor noises to the measurement system. How to overcome or weaken the influence of the sensor noises and the outer disturbances is an important issue to the control system of the engine. To solve this problem, the 2D inner steady flow of hypersonic inlets was numerically simulated in different freestream conditions and backpressures, and two different inlet unstart phenomena were analysed. The membership function for hypersonic inlet start/unstart can be obtained by using probabilistic output support vector machine, and the algorithm of multiple classifiers fusion is introduced. The variations of the classification accuracy with the intensity of the sensor noises and the number of the classifier were discussed respectively. In conclusion, it is useful to introduce the algorithm of support vector machine and multiple classifiers fusion to overcome or weaken the influence of the sensor noises on the classification accuracy of hypersonic inlet start/unstart. The number of the practical fusion classifiers needs a tradeoff between the fusion classification accuracy and the complexity of the classification system.

Type
Research Article
Information
The Aeronautical Journal , Volume 113 , Issue 1142 , April 2009 , pp. 263 - 271
Copyright
Copyright © Royal Aeronautical Society 2009 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Campbell, D.H., F-12 series aircraft propulsion system performance and development, J Aircr, 1974, 11, (11), pp 670–676.Google Scholar
2. Seddon, J. and Goldsmith, E.L., Intake Aerodynamics, 1989, pp 149–168, AIAA Educational Series, AIAA, Washington DC.Google Scholar
3. Mayer, D. and Paynter, G.C., Prediction of supersonic inlet unstart caused by freestream disturbances, AIAA J, 1995, 33, (2), pp 266–275.Google Scholar
4. Mayer, D. and Paynter, G.C., Boundary conditions for unsteady super-sonic inlet analyses, AIAA J, 1994, 32, (6), pp 1200–1206.Google Scholar
5. Neaves, M.D., Mcrae, D.S. and Edwards, J.R., High-speed inlet unstart calculations using an implicit solution adaptive mesh algorithm, January 2001, AIAA Paper 2001-0825.Google Scholar
6. Benson, R.A. and Mcrae, D.S., Numerical simulations of the unstart phenomenon in supersonic inlet/diffuser, June 1993, AIAA Paper 1993-2239.Google Scholar
7. Zha, G.C., Knight, D. and Smith, D., Numerical investigations of high speed civil transport inlet unstart transient at angle of attack, AIAA J Aircr, 1998, 35, (6), pp 851–856.Google Scholar
8. Zha, G.C., Knight, D. and Smith, D., Numerical simulation of high speed civil transport inlet operability with angle of attack, AIAA J Aircr, 1998, 36, (7), pp 1223–1229.Google Scholar
9. Cox, C., Lewis, C. and Pap, R., Prediction of unstart phenomena in hypersonic aircraft, April 1995, AIAA Paper 1995-6018.Google Scholar
10. Chang, J., Yu, D., Bao, W. and Qu, L., Dimensionless analysis of the unstart boundary for 2D mixed hypersonic inlets, Aeronaut J, 2008, 112, (1135), pp 547–555.Google Scholar
11. Schmitz, D.M. and Bissinger, N.C., Design and testing of fixed geometry hypersonic intakes, April 1998, AIAA Paper 1998-1529.Google Scholar
12. Van Wie, D.M. and Kwok, F.T., Starting characteristics of supersonic inlets, July 1996, AIAA Paper 1996-2914.Google Scholar
13. Rreinartz, B.U. and Herrmann, C.D., Aerodynamic performance analysis of a hypersonic inlet isolator using computation and experiment, J Propul and Power, 2003, 19, (5), pp 868–875.Google Scholar
14. Emani, S. and Trexler, C.A., Experimental investigation of inlet combustor isolators for a dual-mode scramjet at a Mach number of 4, May 1995, NASA Technical Paper 3502.Google Scholar
15. Hawkins, W. R.and Marquart, E.J., Two-dimensional generic inlet unstart detection at Mach 2.5-5.0, April 1995, AIAA Paper 1995-6019.Google Scholar
16. Yu, D., Chang, J., Bao, W. and Xie, Z., Optimal classifications criterions of hypersonic inlet start/unstart, J Propul and Power, 2007, 23, (2), pp 310–316.Google Scholar
17. Voland, R.T. and Auslwnder, A.H., CIAM/NASA Mach 6.5 scramjet flight and ground test, November 1999, AIAA Paper 1999-4848.Google Scholar
18. Kulkarni, S.R., Lugosi, G. and Venkatesh, S.S., Learning pattern classification-a survey, IEEE Transactions on Information Theory, 1998, 44, (6), pp 2178–2206.Google Scholar
19. Drikakis, D., Advances in turbulent flow computations using high-resolution methods, Progress in Aerospace Science, 2003, 39, pp 405–424.Google Scholar
20. Herrmann, C.D. and Kosvhel, W.W., Experimental investigation of the internal compression of a hypersonic intake, AIAA Paper 2002-4130.Google Scholar
21. Cui, T., Yu, D., Chang, J. and Bao, W., Topological geometry interpretation of hypersonic inlet start/unstart-catastrophe, hysteresis and bifur-cation, AIAA J Aircr, 2008, 45, (4), pp 1464–1468.Google Scholar
22. Trapier, S., Duveau, P. and Deck, S., Experimental study of supersonic inlet buzz, AIAA J, 2006, 44, (10), pp 2354–2365.Google Scholar
23. Tan, H.J. and Guo, R.W., Experimental study of the unstable-unstarted condition of a hypersonic inlet at Mach 6, J Propulsion and Power, 2007, 23, (4), pp 783–788.Google Scholar
24. Wagner, J.L., Valdivia, A., Yuceil, K.B., Clemens, N.T. and Dolling, D.S., An experimental investigation of supersonic inlet unstart, 2007, AIAA Paper 2007-4352.Google Scholar
25. Lin, C. F.and Wang, S. D., Fuzzy support vector machines, 2002, IEEE Transaction on Neural Networks, 13, (2), pp 464–471.Google Scholar
26. Huang, H.P. and Liu, Y.H., Fuzzy Support vector machines for pattern recognition and data mining, Int J of Fuzzy Systems, 2002, 4, (3), pp 826–836.Google Scholar
27. Xie, Z.X., Hu, Q.H. and Yu, D.R., Fuzzy output support vector machines for classification, advances in natural computation, 2005, First International Conference, Changsha, China, August 2005, pp 1190–1197.Google Scholar
28. Platt, J.C., Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in Large Margin Classifiers, 1999, MIT Press.Google Scholar
29. Battiti, R. and Colla, A., Democracy in neural nets: Voting schemes for classification, Neural Networks, 1994, 7, (4), pp 691–707.Google Scholar
30. Xu, L., Krzyzak, A. and Suen, C.Y., Method for combing multiple classifiers and their applications to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, 1992, 22, (3), pp 418–435.Google Scholar
31. Lv, X.G., Wang, Y.H. and Jain, A.K., Combining classifiers for face recognition, IEEE International Conference on Multimedia & Expo, July 2003, IEEE, Piscataway, NJ, pp 13–16.Google Scholar
32. Dietrich, C., Schwenker, F. and Palm, G., Classification of time series utilizing temporal and decision fusion, February 2001, Proceedings of the Second International Workshop on Multiple Classifier Systems, Springer-Verlag, London, pp 378–387.Google Scholar
33. Nadal, C., Legault, R. and Suen, C.Y., Complementary algorithms for the recognition of totally unconstrained hand written numeral, June 1990, Proceedings of the 10th International Conference on Pattern Recognition, IEEE, Piscataway, NJ, pp 434–449.Google Scholar
34. Skurichina, M. and Duin, R.P.W., Bagging, boosting and the random subspace method for linear classifiers, Pattern Analysis & Applications, 2002, 5, (2), pp 121–135.Google Scholar