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Model calibration and validation are two activities in system model development, and both of them make use of test data. 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. Test resource allocation is conducted before any actual test is performed, and therefore needs to use synthetic data. This paper develops a test resource allocation methodology to make the system response prediction “robust” to test outcome, i.e., insensitive to the variability in test outcome; therefore, consistent system response predictions can be achieved under different test outcomes. This paper analyzes the uncertainty sources in the generation of synthetic data regarding different test conditions, and concludes that the robustness objective can be achieved if the contribution of model parameter uncertainty in the synthetic data can be maximized. Global sensitivity analysis (Sobol’ index) is used to assess this contribution, and to formulate an optimization problem to achieve the desired consistent system response prediction. A simulated annealing algorithm is applied to solve this optimization problem. The proposed method is suitable either when only model calibration tests are considered or when both calibration and validation tests are considered. Two numerical examples are provided to demonstrate the proposed approach.

Robust Resource Allocation for Calibration and Validation Tests