Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison

• Published in: arXiv.org
• Main Authors: Piazzola, Chiara, Tamellini, Lorenzo, Pellegrini, Riccardo, Broglia, Riccardo, Serani, Andrea, Diez, Matteo
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 04.11.2020
• Subjects: Acceleration; Accuracy; Collocation; Computational fluid dynamics; Computer Science - Numerical Analysis; Datasets; Ferries; Finite element method; Mathematics - Numerical Analysis; Probability density functions; Radial basis function; Reynolds averaged Navier-Stokes method; Uncertainty
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2005.07405

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.