Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models

Quadros, Jaimon Dennis and Khan, Sher Afghan and Aabid, Abdul and Baig, Muneer (2023) Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models. Computer Modeling in Engineering & Sciences, 137 (3). pp. 2331-2352. ISSN 1526-1492 E-ISSN 1526-1506

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Abstract

The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length-to-diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with the cavity (βcav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN-based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for the smooth flow of aerodynamic vehicles. Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). The results show that the PCA-BAS-ENN model delivered highly accurate predictions when compared to the other two models. As a result, the advantages of these results are two-fold, providing: (i) a detailed examination of the efficiency of different neural network algorithms in dealing with a genuine aerodynamic problem, and (ii) helpful insights for regulating process variables to improve technological, operational, and financial factors, simultaneously.

Item Type:	Article (Journal)
Uncontrolled Keywords:	High speed flow; Mach number; machine learning; PCA-BAS-ENN algorithm
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Prof. Dr. Sher Afghan Khan
Date Deposited:	13 Jul 2023 09:13
Last Modified:	07 Nov 2023 11:21
URI:	http://irep.iium.edu.my/id/eprint/105495

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