Title: Improved System Identification for Aeroservoelastic Predictions
Author(s): C.R. O'Neill
Date: August 2003
Pages: 167
Formats: pdf (3304 KB)
Abstract:
Modern high performance aerospace vehicles are particularly susceptible to destructive fluid-structure interactions. Accurate and timely aerodynamic predictions are needed for efficient vehicle design and evaluation. System identification offers an efficient and powerful prediction methodology by substituting a trained mathematical system model for the actual aerodynamic system. Coupling the system model with structural and control systems allows for fast and intuitive vehicle analysis. The challenge becomes determining a system model that accurately represents the dominant fluid-flow physics. This thesis investigated linear aerodynamic system identification for aeroservoelastic predictions based on Computational Fluid Dynamics (CFD) flow predictions. The system identification methodology was decomposed into three major areas for further study: aerodynamic system theory, training methodology and excitation signals. A generalized aerodynamics system specification was developed by combining unsteady aerodynamics representations and linear system theory. A linear Auto Regressive Moving Average (ARMA) system model is used for the system representation. A parallel training methodology was developed to decouple complicated systems into multiple simplified systems. A study of excitation signals indicated that an offset frequency-swept chirp improved prediction quality and consistency. Evaluating coupled aerostructural prediction properties appears to be the most robust model performance criterion. Six aeroelastic cases are evaluated. The presented methodologies allowed for improved aeroservoelastic predictions.
Revised: 18 July 2003 [CRO]