A TIPSO-SVM Expert System for Efficient Classification of TSTO Surrogates

Authors: Ali Sarosh, Dong Yun-Feng, Muhammad Umer

Abstract:

Fully reusable spaceplanes do not exist as yet. This implies that design-qualification for optimized highly-integrated forebody-inlet configuration of booster-stage vehicle cannot be based on archival data of other spaceplanes. Therefore, this paper proposes a novel TIPSO-SVM expert system methodology. A non-trivial problem related to optimization and classification of hypersonic forebody-inlet configuration in conjunction with mass-model of the two-stage-to-orbit (TSTO) vehicle is solved. The hybrid-heuristic machine learning methodology is based on two-step improved particle swarm optimizer (TIPSO) algorithm and two-step support vector machine (SVM) data classification method. The efficacy of method is tested by first evolving an optimal configuration for hypersonic compression system using TIPSO algorithm; thereafter, classifying the results using two-step SVM method. In the first step extensive but non-classified mass-model training data for multiple optimized configurations is segregated and pre-classified for learning of SVM algorithm. In second step the TIPSO optimized mass-model data is classified using the SVM classification. Results showed remarkable improvement in configuration and mass-model along with sizing parameters.

Keywords: TIPSO-SVM expert system, TIPSO algorithm, two-step SVM method, aerothermodynamics, mass-modeling, TSTO vehicle.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091268

References:

[1] L. Jian-xia, H. Zhong-xi, and C. Xiao-qing, "Numerical-Study-of-Hypersonic-Glide-Vehicle-Based-On Blunted-Waverider," World Academy of Science, Engineering and Technology, vol. 55, 2011.
[2] A. Charoenpon and E. Pankeaw, "Method-of-Finding-Aerodynamic-Characteristic-Equations-of-Missile-for-Trajectory-Simulation," World Academy of Science, Engineering and Technology, vol. 57, 2011.
[3] J. Cecrdle and J. Malecek, "Conceptual-Design-of-Aeroelastic-Demonstrator-for-Whirl-Flutter-Simulation," World Academy of Science, Engineering and Technology, vol. 68, 2012.
[4] G. Rubio, E. Valero, and S. Lanzan, "Computational-Fluid-Dynamics-Expert-System-using-Artificial-Neural-Networks," World Academy of Science, Engineering and Technology, vol. 63, 2012.
[5] D. Hu, A. Sarosh, and Y.-F. Dong, "An Improved Particle Swarm Optimizer for Parametric Optimization of Flexible Satellite Controller," Applied Mathematics and Computation, vol. 217, pp. 8512-8521, 2011.
[6] A. Sarosh, H. Di, and D. Yun-Feng, "A TIPSO Algorithm Assessment for Aerothermodynamic Optimization of Hypersonic Compression Systems," Engineering Optimization, vol. 45, pp. 591-608, 2013.
[7] Sarosh, D. Yun-Feng, and M. Shoaib, "An Aerothermodynamic and Mass-Model Integrated Optimization Framework for Highly-Integrated Forebody-Inlet Configurations," Applied Mechanics and Materials, vol. 245, pp. 277-282, 2013.
[8] Marsh, P. M. Todd, and G. Gigerenzer, "Cognitive Heuristics," JP Leighton and RJ Sternberg (ed. s), The Nature of Reasoning, Cambridge University Press, Cambridge, MA, USA, pp. 273-287, 2004.
[9] Sarosh, C. Shi-Ming, and D. Yun-Feng, "A Difference-Fractional FOM Decision Method for Down-Selection of Hypersonic Compression System Configurations," Aerospace Science and Technology, 2012.
[10] P. Ortwerth, "Scramjet Flowpath Integration," Scramjet Propulsion, Reston, VA, American Institute of Aeronautics and Astronautics, Inc., 2000, pp. 1105-1293, 2000.