This paper describes initial results from an on-going basic research effort intended to better understand the various static and dynamic force interactions experienced by a test article during wind tunnel experimentation, and how those forces relate to the dynamic attitude of the test article. Coupling among these introduces unsteady aerodynamic effects, inertial bias effects, and/or other sources of errors when determining “static” aerodynamic properties from force balance measurements. Knowledge gained will be used to develop algorithms that allow force balance time history data to be used to determine an improved measure of the true aerodynamic forces and moments acting on the test article. I. Introduction MPROVED understanding of the sources and interactions of various forces and moments experienced during static and dynamic wind tunnel experimentation is needed. Imperfections in the free-stream flow and the rigidity of the test apparatus (test article and support structure) attribute unsteady effects which can lead to inertial biases in the measured loads. During static testing, the practice of filtering and averaging loading data is typically used to remove the time-varying component from the data. However, in the presence of inertial biasing, this data reduction technique can lead to errors especially when considering stringent requirements on lift and drag coefficients. In an on-going project sponsored by the Air Force Office of Scientific Research (AFOSR), Austral Engineering & Software, Inc. (AES) is conducting basic research necessary to better understand the various static and dynamic force interactions experienced by a test article during wind tunnel experimentation, and how those forces relate to the dynamic attitude of the test article. These, in turn, result in unsteady aerodynamic effects, inertial bias effects and/or other sources of errors when determining aerodynamic properties from force balance measurements. Knowledge gained in this endeavor is being used to develop algorithms that allow force balance time history data to be used to arrive at an improved measure of the true aerodynamic forces and moments acting on the test article. A method of correlating multiple force and moment measurements represents the underlying approach used in this effort. Based on knowledge of the structural dynamics, relationships between particular force balance measurements can be exploited for the identification of inertial modes present in the data. Techniques borrowed from the field of communication theory such as matched filter techniques and time-frequency distribution analysis are being investigated. A truth model is being employed for the generation of data for this study. The use of a truth model allows the true aerodynamic and inertial forces to be known such that the results from the data reduction techniques can be validated. Also, the use of a truth model allows the complexity of the problem to be controlled such that the effort can be broken up into incremental steps. II. Truth Model For the initial phase of the effort, a pitch-plane truth model has been developed, which is depicted as a block diagram in Figure 1. The model represents a typical wind tunnel system, focusing on the important interactions between structural and aerodynamic effects under the influence of the unsteady flow always present in the tunnel, and how those effects impact the measurements obtained from the force balance. During the development of the model, representative data were obtained from the Vertical Wind Tunnel (VWT) facility at Wright-Patterson AFB, Ohio. The Structural Model comprises a sting, force balance, and test article (vehicle model) which is collectively I
[1]
M. F. Rubinstein,et al.
Dynamics of structures
,
1964
.
[2]
Khaled H. Hamed,et al.
Time-frequency analysis
,
2003
.
[3]
Frank Steinle,et al.
Progress in Developing a Real-Time Optimal Experiment Control Concept for Wind Tunnel Tests *
,
2004
.
[4]
Alan V. Oppenheim,et al.
Discrete-Time Signal Pro-cessing
,
1989
.
[5]
Athanasios Papoulis,et al.
Probability, Random Variables and Stochastic Processes
,
1965
.
[6]
P. Peebles.
Probability, Random Variables and Random Signal Principles
,
1993
.
[7]
Bruno O. Shubert,et al.
Random variables and stochastic processes
,
1979
.
[8]
Julius S. Bendat,et al.
Engineering Applications of Correlation and Spectral Analysis
,
1980
.