Accelerating Flight Test and Evaluation via Aerodynamic Projection-Based Reduced-Order Models

Reduced-order models (ROMs) based on high-fidelity computational fluid dynamics (CFD) simulations have emerged as promising tools to streamline aerodynamic test and evaluation 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. 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 were validated against one another and against experimental data. The AERO-F solver was then used to construct nonlinear ROMs on a four-dimensional parameter space via the least-squares Petrov–Galerkin method augmented with energy-conserving sampling and weighting hyperreduction. These hyperreduced PROM (HPROM) predictions were compared with flight test data from a T-38C 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 s maneuver, the HPROMs modeled the vertical aerodynamic force acting on the aircraft with less than a 10% error relative to the flight test, correctly identified key T-38 moment and stability characteristics, and provided an 80,000-fold computational speedup over the associated CFD simulations while maintaining 96% accuracy in CFD flow variable predictions.

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Publication Details

Journal
AIAA Journal
Published
2026-07-27
DOI
https://doi.org/10.2514/1.j067099
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Accelerating Flight Test and Evaluation via Aerodynamic Projection-Based Reduced-Order Models

Matthew R. Chmiel, Mehdi Ghoreyshi, Charbel Farhat, Daniel Isaiah Harp
AIAA Journal
Model Reduction and Neural Networks
article

Accelerating Flight Test and Evaluation via Aerodynamic Projection-Based Reduced-Order Models

Matthew R. Chmiel, Mehdi Ghoreyshi, Charbel Farhat, Daniel Isaiah Harp
article en

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 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. 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 were validated against one another and against experimental data. The AERO-F solver was then used to construct nonlinear ROMs on a four-dimensional parameter space via the least-squares Petrov–Galerkin method augmented with energy-conserving sampling and weighting hyperreduction. These hyperreduced PROM (HPROM) predictions were compared with flight test data from a T-38C 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 s maneuver, the HPROMs modeled the vertical aerodynamic force acting on the aircraft with less than a 10% error relative to the flight test, correctly identified key T-38 moment and stability characteristics, and provided an 80,000-fold computational speedup over the associated CFD simulations while maintaining 96% accuracy in CFD flow variable predictions.

AIAA Journal
United States Air Force Academy (US), Stanford University (US)
Office of Naval Research, U.S. Air Force Academy, Air Force Office of Scientific Research
Affordable and clean energy
Openalex Percentile: Top 9%
Model Reduction and Neural Networks
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Accelerating Flight Test and Evaluation via Aerodynamic Projection-Based Reduced-Order Models — Matthew R. Chmiel, Mehdi Ghoreyshi, et al. · AIAA Journal (2026) | TGRS Research Map | TGRS