Test campaign design for model uncertainty reduction

Abstract

Testing or performing inspections to gain information and reduce uncertainty is a necessary activity during the life-cycle of any engineering system. This study develops analytical methods for the optimization of test and inspection campaigns at various system levels in order to reduce the uncertainty of the full system model prediction. The Bayesian network methodology is utilized to connect models, uncertain quantities, testing or inspection data, and various errors in a unified framework so that gaining information at a lower level can be used to reduce the uncertainty of the full system output. Once the Bayesian network is established, different test campaign options can be compared. The testing campaign which is likely to provided data that most efficiently and effectively reduces the uncertainty in the full system output is chosen as optimal. Four methodologies are developed that solve different test selection problems for engineering systems: (1) test-type selection, (2) test input setting design, (3) test campaign design for manufacturing optimization, and (4) inspection type selection during system operation. These methodologies are demonstrated on various multi-physics, multi-scale aerospace application problems including, a thermal vibration problem, a simplified telescope mirror problem, and a fatigue crack growth problem.