Assoc. Prof. Dr. Ali Sarosh

Committee: International Scientific Committee of Transport and Vehicle Engineering
University: National University of Sciences and Technology
Department: Department of Aerospace Engineering
Research Fields: Space Vehicle Design, Space Propulsion, Hypersonic Aerothermodynamics, Flight Vehicle Design, Computational Fluid Dynamics


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

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


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: Aerothermodynamics, TIPSO-SVM expert system, TIPSO algorithm, two-step SVM method, mass-modeling, TSTO vehicle

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