During product development, testing of models and prototypes offers significant advantages over direct product testing, including easier, cheaper, and faster fabrication. However, two issues prevent effective functional testing with prototypes: prediction accuracy and confidence in scale testing results.
The traditional similarity method, which is based on dimensional analysis, is commonly applied to perform scale testing. However, the method may not provide accurate scale testing results, especially when available model materials are different from the final product materials. The authors have developed a new empirical similarity method, wherein specimen pairs and partial knowledge of systems are systematically utilized, to improve the prediction accuracy. In this paper we describe the construction of error measures to utilize scale testing results with confidence.
In practice, scale testing results are validated based on experiences with previous testing results. This approach to predicting accuracy is difficult to formalize. We develop and simulate a systematic two-level error estimation procedure. Realistic numerical examples demonstrate the feasibility of the approach.