Abstract
From a mathematical viewpoint, the frequency domain analysis of vessel motion responses due to wave actions incorporates the integration of system dynamics idealized in terms of response amplitude operators (RAOs) for 6 DOF rigid body motions and an input wave spectrum to yield the response spectrum. Various quantities of interest can be deduced from the response spectrum and further used for decision support in marine operations, extreme value and fatigue analysis. The variation of such quantities, owing to the uncertainties associated with the vessel system parameters, can be quantified by performing uncertainty propagation (UP) and consequent sensitivity analysis (SA). This study, emphasizes and proposes a computational-efficient way of assessing the sensitivity of the system model output with respect to the uncertainties residing in the input parameters by operating on a surrogate model representation. In this respect, the global sensitivity analysis is effectively carried out by deploying an efficient non-intrusive polynomial chaos expansion (PCE) surrogate model built using a point collocation strategy. Successively, the coherent and effective Sobol’ indices are obtained from the analytical decomposition of the polynomial coefficients. The indices, eventually, are employed to quantitatively gauge the effects of input uncertainties on the output 6 DOF vessel responses.
Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam
