Probabilistic Error Estimation In Model-Based Predictions

Modern ship designs can only be successful if the ship can survive in a hostile environment. External threats for a ship can originate from underwater detonations, anti-ship missiles, and even low tech weapons like in the terrorist attack of USS Cole. These threats become even more important as the focal point for naval operations has shifted towards littoral areas, where ships are exposed to a higher risk. Current analysis methods do not provide high level of confidence and large structural design safety factors are used. Thus, ships are heavier and more expensive to construct and maintain than may actually be required. Models that quantify the level of confidence are required in order to provide meaningful and reliable information during the ship design stage. The proposed project will develop a system for probabilistic Error Estimation in model based predictions. It will utilize commercially available non-linear codes for shock analysis (LS-DYNA, ABAQUS, LS-DYNA/USA, etc.), a limited amount of test data, the level of uncertainty in model parameters and in the test data. Based on this information it will update the numerical model for improved probabilistic correlation to the test data and it will provide a probabilistic error estimate for the numerical results.