Abstract
D.Ing. (Mechanical Engineering)
The most common data sought during a wind tunnel test program are the forces and moments acting on an airframe, (or any other test article). The most common source of this data is the internal strain gauge balance. Balances are six degree of freedom force transducers that are required to be of small size and of high strength and stiffness. They are required to deliver the highest possible levels of accuracy and reliability.
There is a focus in both the USA and in Europe to improve the performance of balances through collaborative research. This effort is aimed at materials, design, sensors, electronics calibration systems and calibration analysis methods. Recent developments in the use of statistical methods, including modern design of experiments, have resulted in improved balance calibration models. Research focus on the calibration of six component balances has moved to the determination of the uncertainty of measurements obtained in the wind tunnel. The application of conventional statistically-based approaches to the determination of the uncertainty of a balance measurement is proving problematical, and to some extent an impasse has been reached. The impasse is caused by the rapid expansion of the problem size when standard uncertainty determination approaches are used in a six-degree of freedom system that includes multiple least squares regression and iterative matrix solutions.
This thesis describes how the uncertainty of loads reported by a six component balance can be obtained by applying a direct simulation of the end-to-end data flow of a balance, from calibration through to installation, using a Monte Carlo Simulation. It is postulated that knowledge of the error propagated into the test environment through the balance will influence the choice of calibration model, and that an improved model, compared to that determined by statistical methods without this knowledge, will be obtained.
Statistical approaches to the determination of a balance calibration model are driven by obtaining the best curve-fit statistics possible. This is done by adding as many coefficients to the modelling polynomial as can be statistically defended. This thesis shows that the propagated error will significantly influence the choice of polynomial coefficients. In order to do this a Performance Weighted Efficiency (PWE) parameter is defined. The PWE is a combination of the curve-fit statistic, (the back calculated error for the chosen polynomial), a value representing the overall prediction interval for the model(CI_rand), and a value representing the overall total propagated uncertainty of loads reported by the installed balance...