Adapting second‐order response surface designs to specific needs |
| |
Authors: | James R. Simpson Drew Landman Rupert Giroux Michelle Zeisset Brian Hall Ray D. Rhew |
| |
Affiliation: | 1. Florida State University, Tallahassee, FL 32310, U.S.A.;2. Old Dominion University, Norfolk, VA 23665, U.S.A.;3. NASA Langley, Hampton, VA 23605, U.S.A. |
| |
Abstract: | Experimental design strategies most often involve an initial choice of a classic factorial or response surface design and adapt that design to meet restrictions or unique requirements of the system under study. One such experience is described here, in which the objective was to develop an efficient experimental design strategy that would facilitate building second‐order response models with excellent prediction capabilities. In development, careful consideration was paid to the desirable properties of response surface designs. Once developed, the proposed design was evaluated using Monte Carlo simulation to prove the concept, a pilot implementation of the design carried out to evaluate the accuracy of the response models, and a set of validation runs enacted to look for potential weaknesses in the approach. The purpose of the exercise was to develop a procedure to efficiently and effectively calibrate strain‐gauge balances to be used in wind tunnel testing. The current calibration testing procedure is based on a time‐intensive one‐factor‐at‐a‐time method. In this study, response surface methods were used to reduce the number of calibration runs required during the labor‐intensive heavy load calibration, to leverage the prediction capabilities of response surface designs, and to provide an estimate of uncertainty for the calibration models. Results of the three‐phased approach for design evaluation are presented. The new calibration process will require significantly fewer tests to achieve the same or improved levels of precision in balance calibration. Copyright © 2008 John Wiley & Sons, Ltd. |
| |
Keywords: | response surface methodology second‐order designs Box Behnken prediction variance Monte Carlo simulation desirable design properties |
|
|