Quantification of Experimental Uncertainty in Design Analysis

Uncertainty, being an integral part of any test, experiment or manufacturing process, can be declared as the foremost runner to be analyzed in design analysis. Uncertainty analysis coupled with probability of failure for a component or system gives us enough fuel to ride on towards the optimization process. However, Experimental uncertainty is also a kind of uncertainty which is ignored in modern day uncertainty analysis. This is what motivates my research. In order to quantify or demonstrate the effect of experimental uncertainty we discuss two analysis methods in this paper namely; first order second moment method which can be used to evaluate probability of failure and Worst case analysis method. Consequently, Taylor series error propagation can be applied to compute the error in output which is explained using a comprehensive example. The main focus of this paper is to incorporate experimental uncertainty in uncertainty analysis.