Optimal Classification Criterions of Hypersonic Inlet Start/Unstart

Inlet start/unstart detection is one of the most important issues of hypersonic inlet and is also the foundation of protection control of a scramjet. To solve this problem, the 2-D inner steady flow of a hypersonic inlet was numerically simulated in different freestream conditions and backpressures with a Reynolds-averaged Navier-Stokes solver using a renormalization group k-e turbulence model; two different inlet unstart phenomena were analyzed. The feature selection of the pattern classification of hypersonic inlet start/unstart was performed based on "numerical experimental" data by the support vector machine-recursive feature elimination algorithm. The optimal classification criterions of inlet start/unstart were obtained with the Fisher linear discriminant analysis by maximizing the between-class distance of the inlel/unstart sample set and minimizing the within-class, and the physical significance of the classification criterions was explained. The idea of classification criterions used in the Central Institute of Aviation Motors/NASA flight test and possible reasons why the control system could not properly sense inlet start/unstart were discussed. In conclusion, it is useful to introduce the support vector machine-recursive feature elimination algorithms and the Fisher linear discriminant analysis to acquire the optimal classification criterions of inlet start/unstart.

[1]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[2]  David H. Campbell.  F-12 Series Aircraft Propulsion System Performance and Development , 1973 .

[3]  D. Mcrae,et al.  Numerical simulations of the unstart phenomenon in a supersonic inlet/diffuser , 1993 .

[4]  Isabelle Guyon,et al.  Discovering Informative Patterns and Data Cleaning , 1996, Advances in Knowledge Discovery and Data Mining.

[5]  G. Paynter,et al.  Boundary conditions for unsteady supersonic inlet analyses , 1994 .

[6]  Jing-Yu Yang,et al.  Discriminant performance of the algebraic features of handwritten character images , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[7]  G. Paynter,et al.  Prediction of supersonic inlet unstart caused by freestream disturbances , 1994 .

[8]  C. Lewis,et al.  Prediction of unstart phenomena in hypersonic aircraft , 1995 .

[9]  J. P. Weidner,et al.  Experimental Investigation of Inlet-Combustor Isolators for a Dual-Mode Scramjet at a Mach Number of 4 , 1995 .

[10]  W. Hawkins,et al.  Two-dimensional generic inlet unstart detection at Mach 2.5-5.0 , 1995 .

[11]  D. Wie,et al.  Starting characteristics of supersonic inlets , 1996 .

[12]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[13]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[14]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[15]  D. Schmitz,et al.  Design and testing of 2-D fixed-geometry hypersonic intakes , 1998 .

[16]  D. Knight,et al.  Numerical Simulation of High-Speed Civil Transport Inlet Operability with Angle of Attack , 1998 .

[17]  Sanjeev R. Kulkarni,et al.  Learning Pattern Classification - A Survey , 1998, IEEE Trans. Inf. Theory.

[18]  D. Knight,et al.  Investigations of High-Speed Civil Transport Inlet Unstart at Angle of Attack , 1998 .

[19]  A. Roudakov,et al.  CIAM/NASA Mach 6.5 Scramjet Flight and Ground Test , 1999 .

[20]  Jack R. Edwards,et al.  HIGH-SPEED INLET UNSTART CALCULATIONS USING AN IMPLICIT SOLUTION ADAPTIVE MESH ALGORITHM , 2001 .

[21]  Alain Rakotomamonjy,et al.  Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..

[22]  Huan Liu,et al.  Active Feature Selection Using Classes , 2003, PAKDD.

[23]  Birgit Reinartz,et al.  Aerodynamic Performance Analysis of a Hypersonic Inlet Isolator Using Computation and Experiment , 2003 .

[24]  Wen-kai Lu,et al.  Feature expansion and feature selection for general pattern recognition problems , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[25]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[26]  Jie Yang,et al.  Feature Selection for Multi-class Problems Using Support Vector Machines , 2004, PRICAI.

[27]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[28]  F. Azuaje,et al.  Multiple SVM-RFE for gene selection in cancer classification with expression data , 2005, IEEE Transactions on NanoBioscience.

[29]  Qinghua Hu,et al.  Information-preserving hybrid data reduction based on fuzzy-rough techniques , 2006, Pattern Recognit. Lett..

[30]  W. Bao,et al.  Dimensionless analysis of the unstart boundary for 2D mixed hypersonic inlets , 2008, The Aeronautical Journal (1968).