Application of Projection-Based Reduced-Order Models to the Aerodynamic Test and Evaluation of the T-38 Aircraft

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Authors: Matthew R. Chmiel, Mehdi Ghoreyshi, Daniel I. Harp, Charbel Farhat
Cited by: 2
References: 25

Abstract

Reduced-order models (ROMs) based on high-fidelity computational fluid dynamics (CFD) simulations have emerged as promising tools to streamline aerodynamic test and evaluation (T&E) of air vehicles. This study presents the development and application of projection-based ROMs (PROMs) for the T-38 Talon aircraft, aiming to accelerate flight test campaigns while maintaining predictive accuracy. This paper outlines the modeling framework, cross-code verification strategy, and flight validation results, demonstrating how PROMs can bridge high-fidelity simulations with real-world test data interpretation. Two different CFD solvers -- Stanford's AERO-F and the Department of Defense's Kestrel -- were used to compute the aerodynamic performance of the T-38. Model outputs, including aerodynamic forces, moments, and surface pressure distributions, were validated against one another and against experimental data obtained from the United States Air Force Academy (USAFA) subsonic wind tunnel. The AERO-F solver was then used to construct nonlinear ROMs on a 4-dimensional parameter space via the Least-Squares Petrov-Galerkin (LSPG) method augmented with Energy-Conserving Sampling and Weighting (ECSW) hyperreduction. These hyperreduced PROM (HPROM) predictions were compared with flight test data from a Test Pilot School (TPS) T-38 performing longitudinal maneuvers at Edwards Air Force Base, spanning a challenging range of angles of attack, Mach numbers, altitudes, and stabilator deflections. Over the approximately 90-second test maneuver, the HPROMs modeled the vertical aerodynamic force acting on the aircraft with better than 10% accuracy, correctly identified key T-38 moment and stability characteristics, and provided an 80,000-fold computational speedup over the associated CFD simulations on the same configuration.

Fields of Study & Subjects

Model Reduction and Neural Networks Aerospace and Aviation Technology Computational Fluid Dynamics and Aerodynamics

Publication details

Journal / source
AIAA SCITECH 2026 Forum
Publisher
American Institute of Aeronautics and Astronautics
Publication date
8 January 2026