Evaluation of Reduced Order Aerodynamic Models for Transonic Flow over a Multiple-Swept Wing Configuration

Abstract

Efficient input data generation for reduced-order model applications to accurately predict aerodynamic performance and stability characteristics over a large part of a fighter aircraft's flight envelope is a major challenge. In this paper, aerodynamic reduced-order models are created from two pseudorandom binary sequence (PRBS) training maneuvers. During these maneuvers, the angle of attack and pitch rate change in a periodic and deterministic manner which is characterized by white-noise-like properties. Typical PRBS signals include sudden input variations between two distinct values, such as minimum and maximum angles of attack. However, the signals used in this paper were modified to have the step changes to depend on the simulation time. In the first motion, the aircraft undergoes a signal at a constant Mach number of 0.85. In the second motion, the Mach number varies in an optimized manner from 0.1 to 0.9. The test case is a generic triple-delta wing configuration. Simulations were run using the DoD HPCMP CREATE-AV/Kestrel simulation tools. A prescribed-body motion was used to vary input parameters under given freestream conditions (Mach number and angle of attack). Different reduced-order methods were applied, that comprise regression, feed-forward neural network and auto-regressive surrogate modeling techniques to predict integrated force and moment coefficients and a proper-orthogonal decomposition based neural network approach for surface pressure prediction. Once models of integrated forces and moments were created, they were used to predict static and stability derivatives at different angles of attack and Mach numbers. Models were then used to predict aerodynamic responses to arbitrary motions including pitch sinusoidal, chirp, Schroeder, and step. Model predictions were compared with actual CFD data. Overall, a good agreement was found for all models. Models to predict surface pressure data were also able to accurately predict the upper surface pressure data at different spanwise and chordwise locations at different angles of attack for both static and dynamic runs.