High-speed aerodynamics and dynamic stability of aerospace vehicles: a review of aerodynamic derivatives, flow behavior, and machine-learning approaches

Khetmalis, Varsha and Shaikh, Javed S. and Pathan, Khizar Ahmed and Aabid, Abdul and Khan, Sher Afghan (2026) High-speed aerodynamics and dynamic stability of aerospace vehicles: a review of aerodynamic derivatives, flow behavior, and machine-learning approaches. Fluid Dynamics & Materials Processing, 22 (10). pp. 1-36. ISSN 1555-256X E-ISSN 1555-2578

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Abstract

This review examines recent advances in the aerodynamic analysis and dynamic stability of high-speed aerospace vehicles, with particular emphasis on surface pressure distributions, aerodynamic stability derivatives, and complex flow behavior under supersonic and hypersonic conditions. The review first outlines the fundamental physical mechanisms governing high-speed aerodynamics and then critically surveys analytical, numerical, computational fluid dynamics, and experimental approaches for predicting aerodynamic characteristics. Particular attention is given to pressure, stiffness, damping, and rolling derivatives, as well as flow behavior in suddenly expanded ducts, which are relevant to the aerodynamic performance and stability of high-speed vehicles. The reviewed studies indicate that Mach number, geometric configuration, angle of attack, and pivot position are among the principal parameters governing aerodynamic forces, flow structures, and dynamic stability. Recent developments in machine learning, including artificial neural networks, surrogate modelling, physics-informed neural networks, and reinforcement learning, have demonstrated considerable potential for reducing computational costs and accelerating aerodynamic prediction and optimization. Despite these advances, several challenges remain. In particular, integrated prediction frameworks that combine high-fidelity simulations, experimental measurements, and data-driven methods remain limited, while comprehensive experimental validation under hypersonic conditions is still scarce. The accurate prediction of aerodynamic stability derivatives using machine-learning approaches also requires further development, particularly when multiple derivatives must be predicted simultaneously across a broad range of operating conditions. By critically assessing the field's current state, this review identifies key methodological and experimental gaps and highlights promising directions for developing more accurate, efficient, and reliable approaches to aerodynamic design and dynamic-stability assessment of high-speed aerospace vehicles.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Surface pressure; stability derivatives; machine learning; CFD; supersonic flow; hypersonic flow; aerodynamic prediction
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL500 Aeronautics
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:	23 Oct 2026 16:57
Last Update:	23 Oct 2026 16:57
Queue Number:	2026-10-Q5810
URI:	http://irep.iium.edu.my/id/eprint/132224
Indexed In:	UNSPECIFIED

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