The reverse Taylor impact is a common experiment to investigate the dynamical response of materials at high strain rates. To better understand the physical phenomena and to provide a platform for code validation and Uncertainty Quantification (UQ), a co-designed simulation and experimental paradigm is investigated. For validation under uncertainty, quantities of interest (QOIs) within subregions of the computational domain are introduced. When regions of interest can be identified, the computational cost for UQ can be reduced by confining the random variability within these regions. This observation inspired us to develop an asynchronous space and time computational algorithm with localized UQ. In every region of interest, high resolution space and time discretization schemes are used for a stochastic model. Apart from the regions of interest, low spatial and temporal resolutions are allowed for a stochastic model with low dimensional representation of uncertainty. The approach is exercised on a linear elastodynamics problem and shows a potential in reducing the UQ computational cost.

Uncertainty quantification of the reverse Taylor impact test and localized asynchronous space-time algorithm

Salvadori A.;Lee S.;Matous K.
2018-01-01

Abstract

The reverse Taylor impact is a common experiment to investigate the dynamical response of materials at high strain rates. To better understand the physical phenomena and to provide a platform for code validation and Uncertainty Quantification (UQ), a co-designed simulation and experimental paradigm is investigated. For validation under uncertainty, quantities of interest (QOIs) within subregions of the computational domain are introduced. When regions of interest can be identified, the computational cost for UQ can be reduced by confining the random variability within these regions. This observation inspired us to develop an asynchronous space and time computational algorithm with localized UQ. In every region of interest, high resolution space and time discretization schemes are used for a stochastic model. Apart from the regions of interest, low spatial and temporal resolutions are allowed for a stochastic model with low dimensional representation of uncertainty. The approach is exercised on a linear elastodynamics problem and shows a potential in reducing the UQ computational cost.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/572378
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