Implementation of the Multiscale Stochastic Finite Element Method on Elliptic PDE Problems

In this study, a multi-scale finite element method was proposed to solve two linear scale-coupling stochastic elliptic PDE problems, a tightly stretched wire and flow through porous media. At microscopic level, the main idea was to form coarse-scale equations with a prescribed analytic form that may differ from the underlying fine-scale equations. The relevant stochastic homogenization theory was proposed to model the effective global material coefficient matrix. At the macroscopic level, the Karhunen–Loeve decomposition was coupled with a Polynomial Chaos expansion in conjunction with a Galerkin projection to achieve an efficient implementation of the randomness into the solution procedure. Various stochastic methods were used to plug the microscopic cell to the global system. Strategy and relevant algorithms were developed to boost computational efficiency and to break the curse of dimension. The results of numerical examples were shown consistent with ones from literature. It indicates that the proposed numerical method can act as a paradigm for general stochastic partial differential equations involving multi-scale stochastic data. After some modification, the proposed numerical method could be extended to diverse scientific disciplines such as geophysics, material science, biological systems, chemical physics, oceanography, and astrophysics, etc.

[1]  X. Frank Xu,et al.  Stochastic computation based on orthogonal expansion of random fields , 2011 .

[2]  Sarah C. Baxter,et al.  Simulation of local material properties based on moving-window GMC , 2001 .

[3]  Sei-ichiro Sakata,et al.  Ns-kriging based microstructural optimization applied to minimizing stochastic variation of homogenized elasticity of fiber reinforced composites , 2009 .

[4]  M. Kaminski Homogenization-based finite element analysis of unidirectional composites by classical and multiresolutional techniques , 2005 .

[5]  Mathilde Chevreuil,et al.  A multiscale method with patch for the solution of stochastic partial differential equations with localized uncertainties , 2013 .

[6]  Multiscale stochastic finite element method on random field modeling of geotechnical problems — a fast computing procedure , 2015 .

[7]  K. Sabelfeld,et al.  Elastostatics of a Half-Plane under Random Boundary Excitations , 2009 .

[8]  T. Hughes Multiscale phenomena: Green's functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods , 1995 .

[9]  Armen Der Kiureghian,et al.  The stochastic finite element method in structural reliability , 1988 .

[10]  Lihua Shen,et al.  Multiscale stochastic finite element modeling of random elastic heterogeneous materials , 2010 .

[11]  Marcin Kamiński,et al.  On probabilistic fatigue models for composite materials , 2002 .

[12]  Marcin Kamiński,et al.  Stochastic finite element method homogenization of heat conduction problem in fiber composites , 2001 .

[13]  Xiang Ma,et al.  A stochastic mixed finite element heterogeneous multiscale method for flow in porous media , 2011, J. Comput. Phys..

[14]  Thomas Y. Hou,et al.  Convergence of a multiscale finite element method for elliptic problems with rapidly oscillating coefficients , 1999, Math. Comput..

[15]  Mircea Grigoriu,et al.  Effective conductivity by stochastic reduced order models (SROMs) , 2010 .

[16]  Jack W. Baker,et al.  Characterization of random fields and their impact on the mechanics of geosystems at multiple scales , 2012 .

[17]  Lori Graham-Brady,et al.  A stochastic computational method for evaluation of global and local behavior of random elastic media , 2005 .

[18]  X. F. Xu Generalized Variational Principles for Uncertainty Quantification of Boundary Value Problems of Random Heterogeneous Materials , 2009 .

[19]  Xiang Ma,et al.  An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations , 2009, J. Comput. Phys..

[20]  G. Beylkin,et al.  A Multiresolution Strategy for Numerical Homogenization , 1995 .

[21]  Wing Kam Liu,et al.  Random field finite elements , 1986 .

[22]  Fumihiro Ashida,et al.  A Microscopic failure probability analysis of a unidirectional fiber reinforced composite material via a multiscale stochastic stress analysis for a microscopic random variation of an elastic property , 2012 .

[23]  H. Matthies Stochastic finite elements: Computational approaches to stochastic partial differential equations , 2008 .

[24]  Nicholas Zabaras,et al.  A stochastic variational multiscale method for diffusion in heterogeneous random media , 2006, J. Comput. Phys..

[25]  Roger Ghanem,et al.  Ingredients for a general purpose stochastic finite elements implementation , 1999 .

[26]  Harold S. Park,et al.  An introduction to computational nanomechanics and materials , 2004 .

[27]  R. Caflisch Monte Carlo and quasi-Monte Carlo methods , 1998, Acta Numerica.

[28]  C. Farhat,et al.  Bubble Functions Prompt Unusual Stabilized Finite Element Methods , 1994 .

[29]  S. Sakata,et al.  Stochastic analysis of laminated composite plate considering stochastic homogenization problem , 2015 .

[30]  T. Hughes,et al.  The variational multiscale method—a paradigm for computational mechanics , 1998 .

[31]  H. Matthies,et al.  Uncertainties in probabilistic numerical analysis of structures and solids-Stochastic finite elements , 1997 .

[32]  Lihua Shen,et al.  A Green-function-based multiscale method for uncertainty quantification of finite body random heterogeneous materials , 2009 .

[33]  G. Stefanou The stochastic finite element method: Past, present and future , 2009 .

[34]  Wing Kam Liu,et al.  Probabilistic finite elements for nonlinear structural dynamics , 1986 .

[35]  X. Frank Xu,et al.  A multiscale stochastic finite element method on elliptic problems involving uncertainties , 2007 .

[36]  Fumihiro Ashida,et al.  Stochastic homogenization analysis on elastic properties of fiber reinforced composites using the equivalent inclusion method and perturbation method , 2008 .

[37]  Thomas Gerstner,et al.  Numerical integration using sparse grids , 2004, Numerical Algorithms.

[38]  Marcin Kamiński,et al.  Sensitivity and randomness in homogenization of periodic fiber-reinforced composites via the response function method , 2009 .

[39]  Martin Ostoja-Starzewski,et al.  Stochastic finite elements as a bridge between random material microstructure and global response , 1999 .

[40]  J. Zeman,et al.  Stochastic Modeling of Chaotic Masonry via Mesostructural Characterization , 2008, 0811.0972.

[41]  Jacob Fish,et al.  Discrete-to-continuum bridging based on multigrid principles , 2004 .

[42]  Lori Graham-Brady,et al.  Computational stochastic homogenization of random media elliptic problems using Fourier Galerkin method , 2006 .

[43]  Marcin Kamiński,et al.  Multiscale homogenization of n-component composites with semi-elliptical random interface defects , 2005 .

[44]  Mary F. Wheeler,et al.  Stochastic collocation and mixed finite elements for flow in porous media , 2008 .

[45]  Xi Chen,et al.  Stochastic homogenization of random elastic multi-phase composites and size quantification of representative volume element , 2009 .

[46]  Fumihiro Ashida,et al.  Stochastic homogenization analysis for thermal expansion coefficients of fiber reinforced composites using the equivalent inclusion method with perturbation-based approach , 2010 .

[47]  Hermann G. Matthies,et al.  Solving stochastic systems with low-rank tensor compression , 2012 .

[48]  Wei Wu,et al.  Toward a Nonintrusive Stochastic Multiscale Design System for Composite Materials , 2010 .

[49]  Muneo Hori,et al.  Three‐dimensional stochastic finite element method for elasto‐plastic bodies , 2001 .

[50]  Christian Soize,et al.  Computational nonlinear stochastic homogenization using a nonconcurrent multiscale approach for hyperelastic heterogeneous microstructures analysis , 2012, International Journal for Numerical Methods in Engineering.

[51]  M. Berveiller,et al.  Characterization of random stress fields obtained from polycrystalline aggregate calculations using multi-scale stochastic finite elements , 2015, Frontiers of Structural and Civil Engineering.

[52]  Zhenjun Yang,et al.  A heterogeneous cohesive model for quasi-brittle materials considering spatially varying random fracture properties , 2008 .

[53]  D. Owen,et al.  A priori error estimation for the stochastic perturbation method , 2015 .

[54]  K. Sabelfeld,et al.  Elastic response of a free half-space to random force excitations applied on the boundary , 2012 .

[55]  Marcin Kami ski,et al.  Numerical homogenization ofN-component composites including stochastic interface defects , 2000 .

[56]  M. Kaminski On semi‐analytical probabilistic finite element method for homogenization of the periodic fiber‐reinforced composites , 2011 .

[57]  Hermann G. Matthies,et al.  Sampling-free linear Bayesian update of polynomial chaos representations , 2012, J. Comput. Phys..

[58]  Thomas Y. Hou,et al.  A Multiscale Finite Element Method for Elliptic Problems in Composite Materials and Porous Media , 1997 .

[59]  Song Cen,et al.  Generalized Neumann Expansion and Its Application in Stochastic Finite Element Methods , 2013 .

[60]  Sotirios E. Notaris Integral formulas for Chebyshev polynomials and the error term of interpolatory quadrature formulae for analytic functions , 2006, Math. Comput..

[61]  Baskar Ganapathysubramanian,et al.  A stochastic multiscale framework for modeling flow through random heterogeneous porous media , 2009, J. Comput. Phys..

[62]  Michał Kleiber,et al.  Perturbation based stochastic finite element method for homogenization of two-phase elastic composites , 2000 .

[63]  Fumihiro Ashida,et al.  Hierarchical stochastic homogenization analysis of a particle reinforced composite material considering non-uniform distribution of microscopic random quantities , 2011 .

[64]  George Stefanou,et al.  Simulation of heterogeneous two-phase media using random fields and level sets , 2015 .

[65]  Xiang Ma,et al.  An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations , 2010, J. Comput. Phys..