This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncertainty of the hydrodynamic resistance of a roll-on/roll-off passengers ferry advancing in calm water and subject to two operational uncertainties (ship speed and payload). The first four statistical moments (mean, variance, skewness, kurtosis), and the probability density function for such quantity of interest (QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index Stochastic Collocation (MISC) method and an adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF) algorithm. The QoI is evaluated via computational fluid dynamics simulations, which are performed with the in-house unsteady Reynolds-Averaged Navier-Stokes (RANS) multi-grid solver $\chi$navis. The different fidelities employed by both methods are obtained by stopping the RANS solver at different grid levels of the multi-grid cycle. The performance of both methods are presented and discussed: in a nutshell, the findings suggest that, at least for the current implementations of both algorithms, MISC could be preferred whenever a limited computational budget is available, whereas for a larger computational budget SRBFs seem to be preferable, thanks to its robustness to the numerical noise in the evaluations of the QoI.

翻译：本文比较了两种多纤维化方法,用于对海军工程问题进行前期不确定性量化。具体地说,我们考虑量化在平静的水中前进的滚动/滚动轮渡轮渡的流体动力阻力的不确定性,并有两种操作不确定性(船速和有效载荷)。头四个统计时刻(平均值、差异、变异、斜度、库托克斯)和这种数量的兴趣的概率密度函数(QoI)是用两种多纤维化方法(QoI)计算出来的。两种方法都采用了两种多纤维化方法,即多纤维化托盘配置(MISC)方法和适应性多纤维化堆积基功能(SRBF)算法。通过计算流动动态模拟(船舶速度和有效载荷)来评估QoI。 两种方法的性能都通过内部不稳定的Reynolds-Averfored Navier-Stokes(RANS)多电网解解解(QoI) 和多纤维化(Qchi$navision) 。两种方法的不同准确性是用在多格化电网化电网化层次的不同电解解算法水平上获得的解解解(RANSS)方法和适应性调调调调多纤维调多纤维调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调算算算算算算法。在目前的算法的算法的算法的算法的计算方法的功能的功能性能的性能的性方法的性,在现有的算法时,在现有的算法的运行时,在现有的算法中,在现有的算法中,在现有的算法中,在现有的算法中,在现有的算法的运行法的运行法的运行法的运行法的运行法的运行法的性学上,在现有的的运行法的运行法的运行是在现有的的演算法的演算法的演时,在现有的的演算法的演算法的演算法的演算法的演时,在现有的的演算法的演时,在