Integrated CFD and machine learning investigation of pressure and temperature distributions on a cone at supersonic and hypersonic Mach numbers

Tajanpure, Hemant and Kumar, Krishna and Pathan, Khizer Ahmed and Khan, Sher Afghan and Hussain, Fayaz and Choon, Kit Chan and Rajendaran, Parvathy and Asif, Mohammad (2026) Integrated CFD and machine learning investigation of pressure and temperature distributions on a cone at supersonic and hypersonic Mach numbers. Aerospace Science and Technology, 179 (113393). pp. 1-29. ISSN 1270-9638 E-ISSN 1626-3219

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Abstract

A comprehensive Computational Fluid Dynamics (CFD) and Unified Machine Learning (ML) framework is developed to study high-speed flow regimes over a cone. The study evaluates the distributions of total pressure and total temperature along the slant length of the cone for freestream Mach numbers between 4.2 and 6 and semi-cone angles between 2° and 20°. Flow properties are extracted at non-dimensional positions (x/L) across the range 0 to 1 in 0.1 increments. The CFD results show that the cone tip experiences a significant total pressure loss due to intense shock interactions, leading to downstream pressure stabilization. The total temperature shows a gradual change pattern, resulting from both viscous dissipation and boundary-layer growth, which becomes more pronounced with increasing Mach number and larger cone angles. The CFD results are validated against the Taylor-Maccoll theory. The machine learning models use Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Ordinal Logistic Regression (OLR) to train on CFD data, employing k-fold cross-validation. Later, the models' generalizability was tested on the test dataset. The analysis shows that SVM achieves the best total pressure prediction accuracy, while OLR outperforms other methods in total temperature prediction accuracy. KNN demonstrates moderate accuracy but struggles to generalize to new situations. The combined CFD–ML solution enables researchers to achieve their results with less computational work while delivering accurate predictions. It would serve as a powerful resource for high-speed aerodynamic research and design optimization.

Item Type:	Article (Journal)
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
Depositing User:	Prof. Dr. Sher Afghan Khan
Date Deposited:	17 Aug 2026 23:07
Last Update:	17 Aug 2026 23:07
Queue Number:	2026-08-Q4759
URI:	http://irep.iium.edu.my/id/eprint/130823

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