Certification of projection‐based reduced order modelling in computational homogenisation by the constitutive relation error

In this paper, we propose upper and lower error bounding techniques for reduced order modelling applied to the computational homogenisation of random composites. The upper bound relies on the construction of a reduced model for the stress field. Upon ensuring that the reduced stress satisfies the equilibrium in the finite element sense, the desired bounding property is obtained. The lower bound is obtained by defining a hierarchical enriched reduced model for the displacement. We show that the sharpness of both error estimates can be seamlessly controlled by adapting the parameters of the corresponding reduced order model.

[1]  Juan J. Alonso,et al.  Investigation of non-linear projection for POD based reduced order models for Aerodynamics , 2001 .

[2]  Kari Karhunen,et al.  Über lineare Methoden in der Wahrscheinlichkeitsrechnung , 1947 .

[3]  Timon Rabczuk,et al.  STATISTICAL EXTRACTION OF PROCESS ZONES AND REPRESENTATIVE SUBSPACES IN FRACTURE OF RANDOM COMPOSITES , 2012, 1203.2487.

[4]  Mario Ohlberger,et al.  Error Control Based Model Reduction for Parameter Optimization of Elliptic Homogenization Problems , 2013 .

[5]  P. Ladevèze,et al.  The LATIN multiscale computational method and the Proper Generalized Decomposition , 2010 .

[6]  Elías Cueto,et al.  Proper generalized decomposition of multiscale models , 2010 .

[7]  A. Abdulle,et al.  Reduced basis finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems , 2014 .

[8]  D. Sorensen,et al.  Approximation of large-scale dynamical systems: an overview , 2004 .

[9]  Antonio Huerta,et al.  The computation of bounds for linear-functional outputs of weak solutions to the two-dimensional elasticity equations , 2006 .

[10]  C. Gogu,et al.  Efficient surrogate construction by combining response surface methodology and reduced order modeling , 2013, Structural and Multidisciplinary Optimization.

[11]  L. Sirovich Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .

[12]  Stefan Volkwein,et al.  Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics , 2002, SIAM J. Numer. Anal..

[13]  V. G. Kouznetsova,et al.  Multi-scale computational homogenization: Trends and challenges , 2010, J. Comput. Appl. Math..

[14]  Jacob Fish,et al.  Eigendeformation-based reduced order homogenization for failure analysis of heterogeneous materials , 2007 .

[15]  J. Oden,et al.  A Posteriori Error Estimation in Finite Element Analysis , 2000 .

[16]  Marcus Meyer,et al.  Efficient model reduction in non-linear dynamics using the Karhunen-Loève expansion and dual-weighted-residual methods , 2003 .

[17]  Danny C. Sorensen,et al.  Nonlinear Model Reduction via Discrete Empirical Interpolation , 2010, SIAM J. Sci. Comput..

[18]  Julien Yvonnet,et al.  The reduced model multiscale method (R3M) for the non-linear homogenization of hyperelastic media at finite strains , 2007, J. Comput. Phys..

[19]  Pierre Ladevèze,et al.  Upper error bounds on calculated outputs of interest for linear and nonlinear structural problems , 2006 .

[20]  Pierre Ladevèze,et al.  ERROR ESTIMATION AND MESH OPTIMIZATION FOR CLASSICAL FINITE ELEMENTS , 1991 .

[21]  C. Farhat,et al.  Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity , 2008 .

[22]  Long Chen FINITE ELEMENT METHOD , 2013 .

[23]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[24]  David Ryckelynck Hyper‐reduction of mechanical models involving internal variables , 2009 .

[25]  C. Farhat,et al.  Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations , 2011 .

[26]  Wojtek J. Krzanowski,et al.  Cross-Validation in Principal Component Analysis , 1987 .

[27]  A. Patera,et al.  A Successive Constraint Linear Optimization Method for Lower Bounds of Parametric Coercivity and Inf-Sup Stability Constants , 2007 .

[28]  J. Oden,et al.  A Posteriori Error Estimation in Finite Element Analysis: Oden/A Posteriori , 2000 .

[29]  D. Rovas,et al.  Reliable Real-Time Solution of Parametrized Partial Differential Equations: Reduced-Basis Output Bound Methods , 2002 .

[30]  Jacob Fish,et al.  Bridging the scales in nano engineering and science , 2006 .

[31]  P Kerfriden,et al.  Bridging Proper Orthogonal Decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems. , 2011, Computer methods in applied mechanics and engineering.

[32]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[33]  Peter Wriggers,et al.  An Introduction to Computational Micromechanics , 2004 .

[34]  Anthony T. Patera,et al.  "Natural norm" a posteriori error estimators for reduced basis approximations , 2006, J. Comput. Phys..

[35]  Philippe G. Ciarlet,et al.  The finite element method for elliptic problems , 2002, Classics in applied mathematics.

[36]  Timo Tonn,et al.  Comparison of the reduced-basis and POD a posteriori error estimators for an elliptic linear-quadratic optimal control problem , 2011 .

[37]  Gui-Rong Liu,et al.  Rapid identification of material properties of the interface tissue in dental implant systems using reduced basis method , 2013 .

[38]  Francisco Chinesta,et al.  Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models , 2010 .

[39]  Ludovic Chamoin,et al.  On the techniques for constructing admissible stress fields in model verification: Performances on engineering examples , 2011, 1704.06680.

[40]  R. Verfürth A review of a posteriori error estimation techniques for elasticity problems , 1999 .

[41]  P. Sagaut,et al.  Towards an adaptive POD/SVD surrogate model for aeronautic design , 2011 .

[42]  Siamak Niroomandi,et al.  Model order reduction for hyperelastic materials , 2010 .

[43]  N. Nguyen,et al.  An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations , 2004 .

[44]  Assyr Abdulle,et al.  Adaptive reduced basis finite element heterogeneous multiscale method , 2013 .

[45]  F. F. Ling,et al.  Mastering Calculations in Linear and Nonlinear Mechanics , 2005 .

[46]  Sébastien Boyaval Reduced-Basis Approach for Homogenization beyond the Periodic Setting , 2008, Multiscale Model. Simul..

[47]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[48]  J. Oden,et al.  Goal-oriented error estimation and adaptivity for the finite element method , 2001 .

[49]  P Kerfriden,et al.  A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics. , 2012, Computer methods in applied mechanics and engineering.

[50]  Pierre Ladevèze,et al.  On the verification of model reduction methods based on the proper generalized decomposition , 2011 .

[51]  Elías Cueto,et al.  Proper generalized decomposition of time‐multiscale models , 2012 .

[52]  J. Tinsley Oden,et al.  Advances in adaptive computational methods in mechanics , 1998 .

[53]  A. Nouy A priori model reduction through Proper Generalized Decomposition for solving time-dependent partial differential equations , 2010 .

[54]  Peter Hansbo,et al.  Strategies for computing goal‐oriented a posteriori error measures in non‐linear elasticity , 2002 .

[55]  S. Nemat-Nasser,et al.  Micromechanics: Overall Properties of Heterogeneous Materials , 1993 .

[56]  K. Kunisch,et al.  Optimal snapshot location for computing POD basis functions , 2010 .

[57]  F. J. Fuenmayor,et al.  Mesh adaptivity driven by goal-oriented locally equilibrated superconvergent patch recovery , 2012, 1209.3102.