Machine learning applications in modelling and analysis of base pressure in suddenly, expanded flows

Jaimon, Dennis Quadros and Khan, Sher Afghan and Aabid, Abdul and Alam, Mohammad Shohag and Baig, Muneer (2021) Machine learning applications in modelling and analysis of base pressure in suddenly, expanded flows. Aerospace, 8 (318). pp. 1-23. ISSN 2226-4310

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Abstract

Base pressure becomes a decisive factor in governing the base drag of aerodynamic vehicles. While several experimental and numerical methods have already been used for base pressure analysis in suddenly expanded flows, their implementation is time‐consuming. Therefore, we must develop a progressive approach to determine base pressure (β). Furthermore, a direct consideration of the influence of flow and geometric parameters cannot be studied by using these methods. This study develops a platform for data‐driven analysis of base pressure (β) prediction in suddenly expanded flows, in which the influence of flow and geometric parameters including Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (φ) have been studied. Three different machine learning (ML) models, namely, artificial neural networks (ANN), support vector machine (SVM), and random forest (RF), have been trained using a large amount of data developed from response equations. The response equations for base pressure (β) were created using the response surface methodology (RSM) approach. The predicted results are compared with the experimental results to validate the proposed platform. The results obtained from this work can be applied in the right way to maximize base pressure in rockets and missiles to minimize base drag.

Item Type:	Article (Journal)
Uncontrolled Keywords:	: base pressure; machine learning; artificial neural networks; support vector machine; random forest; response surface methodology
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL780 Rockets
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering Kulliyyah of Engineering
Depositing User:	Prof. Dr. Sher Afghan Khan
Date Deposited:	28 Oct 2021 08:09
Last Modified:	25 Nov 2021 10:31
URI:	http://irep.iium.edu.my/id/eprint/93345

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