Validation of high-fidelity uncertainty quantification of a high-speed catamaran in irregular waves

The validation of a high-fidelity uncertainty quantification of a high-speed catamaran is presented, with focus on irregular wave analysis and approximation methods used in design optimization studies, namely a deterministic regular wave approximation, and a stochastic regular wave UQ model. The approach includes a priori CFD simulations, followed by the ex post facto EFD campaign. The validation variables are the wave elevation, x-force, heave and pitch motions, vertical acceleration of the bridge and vertical velocity of the flight deck. Time series values are addressed as primary variables, whereas mean-crossing amplitudes, height, and period are indicated as secondary variables. The subseries and bootstrap methods are applied in order to estimate the validation values and 95% confidence intervals for expected value (EV), standard deviation (SD), mode, and quantile function. Additionally, validation values and confidence intervals for time series EV and SD are evaluated by time series theory, based on the sample variance and size, and the autocovariance function. The deterministic regular wave model assesses the EV of the x-force, whereas the stochastic regular wave UQ focuses on SSAs of pitch, acceleration, and velocity, as relevant merit factors for design optimization. On average, all variables are validated, but the validation uncertainty is quite large, due to limited number of inlet wave components of CFD and short run length for both CFD and EFD. For the evaluation of confidence intervals, the subseries method is found on average more conservative for EV and less conservative for SD, compared to time series theory. Primary variables are validated with an average 5% error and 37% confidence interval. Secondary variables are validated with an average 21% error and 38% confidence interval. The regular wave model is validated with nearly 9% error and 15% confidence interval, whereas the UQ model is validated with an average 1% error and 30% confidence interval.