  This paper presents a comparison of two methods for the forward uncertainty
quantification (UQ) of complex industrial problems. Specifically, the
performance of Multi-Index Stochastic Collocation (MISC) and adaptive
multi-fidelity Stochastic Radial Basis Functions (SRBF) surrogates is assessed
for the UQ of a roll-on/roll-off passengers ferry advancing in calm water and
subject to two operational uncertainties, namely the ship speed and draught.
The estimation of expected value, standard deviation, and probability density
function of the (model-scale) resistance is presented and discussed; the
required simulations are obtained by the in-house unsteady multi-grid Reynolds
Averaged Navier-Stokes (RANS) solver $\chi$navis. Both MISC and SRBF use as
multi-fidelity levels the evaluations on the different grid levels
intrinsically employed by the RANS solver for multi-grid acceleration; four
grid levels are used here, obtained as isotropic coarsening of the initial
finest mesh. The results suggest that MISC could be preferred when only limited
data sets are available. For larger data sets both MISC and SRBF represent a
valid option, with a slight preference for SRBF, due to its robustness to
noise.
