Si avvisa che in data 10/7/2020 alle ore 12:00, nell'ambito dei seminari online dell'Istituto di Ingegneria del Mare (CNR-INM) si svolgera' il seminario dettagliato nel seguito. Il seminario si svolgera' sulla piattaforma Google Meet, al link

https://meet.google.com/eer-yjqm-dbt

Ulteriori informazioni sulla pagina internet

http://www.inm.cnr.it/2020/06/24/imati-and-inm-joint-effort-uncertainty-quantification-for-ship-hydrodynamics-via-multi-fidelity-methods-with-multi-grid-rans-computations/
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Titolo: Highlights from “Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison,” 21th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (MA&O), AVIATION 2020, June 15-19, 2020

Relatori: Chiara Piazzola, Lorenzo Tamellini, Riccardo Pellegrini, Riccardo Broglia, Andrea Serani Matteo Diez

Abstract: The seminar presents a recent study by the IMATI/INM working group on uncertainty quantification (UQ). A comparison of two methods for the forward UQ of complex industrial problems is presented. 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, obtained by multi-grid Reynolds averaged Navier-Stokes (RANS) computations. Both MISC and SRBF use as multi-fidelity levels the evaluations on 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.

Paper: https://arxiv.org/abs/2005.07405
AIAA online presentation: https://youtu.be/wrnQL571X3s