Uncertainty quantification of Delft catamaran resistance, sinkage and trim for variable Froude number and geometry using metamodels, quadrature and Karhunen-Loève expansion

A framework for assessing convergence and validation of non-intrusive uncertainty quantification (UQ) methods is studied and applied to a complex industrial problem in ship design, namely the high-speed Delft Catamaran advancing in calm water, with variable Froude number and geometry. Relationship between UQ studies and deterministic verification and validation is discussed. Computations are performed using high- (URANS) and low- (potential flow) fidelity simulations. Froude number has expected value and standard deviation equal to 0.5 and 0.05, respectively, on a truncated normal distribution. Geometric uncertainty is related to the research space of a simulation-based design optimization, and assessed through the Karhunen-Loève expansion (KLE). Monte Carlo method with Latin hypercube sampling (MC-LHS) is used to compute expected value, standard deviation, distribution and uncertainty intervals for resistance, sinkage and trim. MC-LHS with CFD is used as a benchmark for validating less costly UQ methods, including MC-LHS with metamodels and standard quadrature formulas. Gaussian quadrature is found the most efficient method; however, MC-LHS with metamodels is preferred since provides with confidence intervals and distributions in a straightforward way and at reasonably small computational cost. UQ results are compared to earlier deterministic single- and multi-objective optimization; reduced-dimensional KLE studies for geometric variability indicate that stochastic optimization would not be of great benefit for the present problem. © 2013 JASNAOE.