Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method

Journal Article · · Journal of Computational Physics
 [1];  [1]
  1. Materials Process Design and Control Laboratory, Sibley School of Mechanical and Aerospace Engineering, 188 Frank H.T., Rhodes Hall, Cornell University, Ithaca, NY 14853-3801 (United States)

In recent years, there has been intense interest in understanding various physical phenomena in random heterogeneous media. Any accurate description/simulation of a process in such media has to satisfactorily account for the twin issues of randomness as well as the multilength scale variations in the material properties. An accurate model of the material property variation in the system is an important prerequisite towards complete characterization of the system response. We propose a general methodology to construct a data-driven, reduced-order model to describe property variations in realistic heterogeneous media. This reduced-order model then serves as the input to the stochastic partial differential equation describing thermal diffusion through random heterogeneous media. A decoupled scheme is used to tackle the problems of stochasticity and multilength scale variations in properties. A sparse-grid collocation strategy is utilized to reduce the solution of the stochastic partial differential equation to a set of deterministic problems. A variational multiscale method with explicit subgrid modeling is used to solve these deterministic problems. An illustrative example using experimental data is provided to showcase the effectiveness of the proposed methodology.

OSTI ID:
21028261
Journal Information:
Journal of Computational Physics, Journal Name: Journal of Computational Physics Journal Issue: 1 Vol. 226; ISSN JCTPAH; ISSN 0021-9991
Country of Publication:
United States
Language:
English

Similar Records

A non-linear dimension reduction methodology for generating data-driven stochastic input models
Journal Article · Fri Jun 20 00:00:00 EDT 2008 · Journal of Computational Physics · OSTI ID:21159397

A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach
Journal Article · Sun Apr 20 00:00:00 EDT 2008 · Journal of Computational Physics · OSTI ID:21159429

Sparse grid collocation schemes for stochastic natural convection problems
Journal Article · Sun Jul 01 00:00:00 EDT 2007 · Journal of Computational Physics · OSTI ID:20991599