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UID:MEC-45e03f0f460f42940faebd081950bf57@matematica.unipv.it
DTSTART:20210330T130000Z
DTEND:20210330T140000Z
DTSTAMP:20210211T085400Z
CREATED:20210211
LAST-MODIFIED:20210211
SUMMARY:Multi-fidelity computational approaches for the uncertainty quantification of ship performance
DESCRIPTION:Multi-fidelity computational approaches for the uncertainty quantification of ship performance\nSpeaker: Chiara Piazzola (IMATI-CNR)\nSeminario online.\nAbstract. Ship performance depends on design and operational parameters (e.g. sea state, advancement speed, payload) which are intrinsically uncertain. Therefore, such uncertainties have to be taken into account when estimating ship performance indicators, such as the resistance to advancement. The assessment of the impact of the uncertainties on the performance indicators is known as forward Uncertainty Quantification (UQ) analysis.\nIn this talk we present a comparison of two methods for the forward UQ analysis of a passegers ferry advancing in calm water and subject to two operational uncertainties, namely the ship speed and payload. Upon choosing a configuration, i.e., fixing these two parameters, the resistance to advancement can be obtained by solving the free-surface Navier-Stokes equations. To this end, we employ a multi-grid Reynolds Averaged Navier-Stokes (RANS) solver. A UQ analysis typically requires solving several configurations of the parameters, and therefore can become very expensive.\nThe two UQ methods compared in this work are the Multi-Index Stochastic Collocation (MISC) and the multi-fidelity Stochastic Radial Basis Functions (SRBF). The estimation of the expected value of the (model-scale) resistance to advancement, as well as of its higher order moments and probability density function, are presented and discussed.\nBoth MISC and SRBF are multi-fidelity methods, i.e., they explore the variability of the resistance to advancement by considering an ensemble of RANS solvers with different accuracy levels (in other words, employing meshes with different resolutions). More precisely, in a first step they query the low-fidelity, and hence inexpensive, solvers for several different configurations of the ferry operational parameters, and perform the UQ analysis based on these configurations. The results thus obtained are then corrected by further solving a handful of configurations over the high-fidelity, and hence expensive, solvers.\nReferences:\n[1] C. Piazzola et al. Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison. Proceedings of the AIAA Aviation Forum 2020.\n[2] J. Beck et al. IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains. Computer Methods in Applied Mechanics and Engineering, 2019.\n[3]J. Wackers et al. Adaptive N-Fidelity Metamodels for Noisy CFD Data. Proceedings of the AIAA Aviation Forum 2020.\n
URL:https://matematica.unipv.it/events/multi-fidelity-computational-approaches-for-the-uncertainty-quantification-of-ship-performance/
ORGANIZER;CN=:MAILTO:
CATEGORIES:Seminari di matematica applicata
LOCATION:Online
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