Aerodynamic Modeling and System Identification from Flight Data-Recent Applications at DLR

Abstract

This paper provides an overview of a few selected system-identification activities carried out during the last decade at DLR, the German Aerospace Center. After a brief account of parameter-estimation methods and of consistency checking of recorded flight data, an emphasis is placed on 1) development of global, high-fidelity aerodynamic databases for training simulators; 2) models for in-flight simulator; 3) modeling of stall hysteresis; 4) estimation of aerodynamic flowfield characteristics and validation of aircraft reaction models from wake vortex encounter flights; and 5) a parametric wake distortion model to improve helicopter coupling off-axis response. These examples cover a variety of flight vehicles with widely different characteristics, namely, the military transport aircraft Transall C-160, commuter aircraft Dornier 328, in-flight simulator VFW-614 ATTAS, high-altitude research aircraft G850 Strato 2C, Airbus-type aircraft, fly-by-wire helicopter BO-105, and fly-by-light helicopter EC-135. Besides parameter estimation aspects, those related to model validation are discussed. Through succinct examples it is brought out that system identification has become an indispensable tool to support not only research but also industry activities in various key areas such as model validation, handling qualities evaluation, control law design, and flight-vehicle design and certification. Thus, it contributes significantly to risk and cost reduction.

Simulation Study of Severity and Mitigation of Wake-Vortex Encounters in Ground Proximity

“…This method was deemed feasible by Barrows [34], verified against wind-tunnel tests by de Bruin [35], and further validated using flight-test data by Fischenberg [36] and Jategaonkar et al [37].…”

High Resolution Simulation of Full Aircraft Control at Flight Reynolds Numbers

“…SID is usually applied to wind tunnel and flight test data to obtain accurate and comprehensive mathematical models of aircraft aerodynamics, for aircraft flight simulation, control system design and evaluation, and dynamic analysis. A very comprehensive review of SID applied to aircraft can be found in Morelli and Klein [9,10] and Jategaonkar [11,12] . Aircraft system identification can be used in cooperative approaches with CFD, to take advantage of the strength of both approaches or having one approach fill in the gaps where the other cannot be used effectively [9] .…”

Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification

“…In system theory, this process is known as system identification and the objective is to obtain dynamic models from observed input and output signals [3]. In particular, system identification methods have been used for flight-test evaluations [4][5][6][7][8][9][10], control analysis and design [11,12] and advanced simulation [13][14][15].…”