Robust Test Resource Allocation using Global Sensitivity Analysis

Abstract

To predict the response of a system with unknown parameters, a common route is to quantify the parameters using test data and propagate the results through a computational model of the system. Activities in this process may include model calibration and/or model validation. Test data value uncertainty has a significant effect on model calibration and model validation, and therefore affects the response prediction. Limited testing budget creates the challenge of test resource allocation, i.e., how to optimize the number of calibration and validation tests to be conducted. In this paper, a novel computational technique based on pseudo-random numbers is proposed to efficiently quantify the uncertainty in the data value of each type of test. This technique helps quantifying the contribution of data value uncertainty to the uncertainty in the prediction through Sobol indices. Consistent predictions using different sets of data are expected if this contribution is small. Then the numbers of each type of test are optimized to minimize this contribution. A simulated annealing algorithm is applied to solve this discrete optimization problem.