Time-Domain Training Signals Comparison for Computational Fluid Dynamics Based Aerodynamic Identification

摘要

Three classes of input training signals are evaluated for computational fluid dynamics based aeroelastic prediction performance. Binary signals, frequency sweeps, and multisines are reviewed and evaluated for aeroelastic prediction performance. Aerodynamic requirements are developed for input signal design and implementation. The input training signals are evaluated with signal property analysis and aeroelastic stability predictions. Five specific input signals were tested with the AGARD 445.6 aeroelastic testcase at Mach 0.90. The signals are the binary 3211 multistep, the frequency swept chirp, the frequency swept offset dc-chirp, the frequency swept Fresnel chirp and the multisine Schroeder sweep. The offset dc-chirp gave the best performance.