Microstructure model reduction and uncertainty quantification in multiscale deformation processes

2010 ◽  
Vol 48 (2) ◽  
pp. 213-227 ◽  
Author(s):  
Babak Kouchmeshky ◽  
Nicholas Zabaras
Keyword(s):  
Uncertainty Quantification ◽  
Model Reduction ◽  
Deformation Processes ◽  
Microstructure Model

Multi-Frequency Model Reduction for Uncertainty Quantification in Computational Vibroacoustics of Automobiles

2020 ◽  
Author(s):  
Justin Reyes ◽  
Laurent Gagliardini ◽  
Christophe Desceliers ◽  
Christian Soize
Keyword(s):  
Uncertainty Quantification ◽  
Model Reduction ◽  
Frequency Model

A Stochastic Galerkin Approach to Uncertainty Quantification in Poroelastic Media

2014 ◽  
Author(s):  
Paul Delgado ◽  
Vinod Kumar
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Expansion Method ◽  
Collocation Methods ◽  
Galerkin Projection ◽  
Material Parameters ◽  
Poroelastic Media ◽  
Oil And Natural Gas ◽  
Deformation Processes ◽  
Stochastic Galerkin

Recent concerns over the safety of oil and natural gas extraction, fracking, and carbon sequestration have driven the need to develop methods for uncertainty quantification for coupled subsurface flow & deformation processes. Traditional monte-carlo methods are versatile, but exhibit prohibitively slow convergence. In this work, we develop an intrusive polynomial chaos expansion method for Biot’s Poroelasticity Equations based on galerkin projection with uniform and log-normally distributed material parameters. We analyze accuracy and efficiency of our method and compare it to monte-carlo and anova based probabilistic collocation methods.

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