×

On stochastic FEM based computational homogenization of magneto-active heterogeneous materials with random microstructure. (English) Zbl 1398.74392

Summary: In the current work we apply the stochastic version of the FEM to the homogenization of magneto-elastic heterogeneous materials with random microstructure. The main aim of this study is to capture accurately the discontinuities appearing at matrix-inclusion interfaces. We demonstrate and compare three different techniques proposed in the literature for the purely mechanical problem, i.e. global, local and enriched stochastic basis functions. Moreover, we demonstrate the implementation of the isoparametric concept in the enlarged physical-stochastic product space. The Gauss integration rule in this multidimensional space is discussed. In order to design a realistic stochastic Representative Volume Element we analyze actual scans obtained by electron microscopy and provide numerical studies of the micro particle distribution. The SFEM framework described in our previous work [Comput. Mech. 57, No. 1, 123–147 (2016; Zbl 1381.74179)] is extended to the case of the magneto-elastic materials. To this end, the magneto-elastic energy function is used, and the corresponding hyper-tensors of the magneto-elastic problem are introduced. In order to estimate the methods’ accuracy we performed a set of simulations for elastic and magneto-elastic problems using three different SFEM modifications. All results are compared with “brute-force” Monte-Carlo simulations used as reference solution.

MSC:

74S05 Finite element methods applied to problems in solid mechanics
65N30 Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs
74Q05 Homogenization in equilibrium problems of solid mechanics
35R60 PDEs with randomness, stochastic partial differential equations
65C30 Numerical solutions to stochastic differential and integral equations

Citations:

Zbl 1381.74179
Full Text: DOI

References:

[1] Adhikari S (2011) A reduced spectral function approach for the stochastic finite element analysis. Comput Methods Appl Mech Eng 200(21-22):1804-1821 · Zbl 1228.74075 · doi:10.1016/j.cma.2011.01.015
[2] Alsayednoor J, Harrison P, Guo Z (2013) Large strain compressive response of 2-d periodic representative volume element for random foam microstructures. Mech Mater 66:7-20 · doi:10.1016/j.mechmat.2013.06.006
[3] Andrianov I, Danishevsky V, Tokarzewski S (2000) Quasifractional approximants in the theory of composite materials. Acta Appl Math 61(1-3):29-35 · Zbl 0958.80001 · doi:10.1023/A:1006455311626
[4] Andrianov I, Danishevs’kyy VV, Weichert D (2008) Simple estimation on effective transport properties of a random composite material with cylindrical fibres. Z Angew Math Phys 59(5):889-903 · Zbl 1157.74033 · doi:10.1007/s00033-007-6146-3
[5] Andrianov IV, Danishevs’kyy VV, Kholod EG (2012) Homogenization of viscoelastic composites with fibres of diamond-shaped cross-section. Acta Mech 223(5):1093-1100 · Zbl 1401.74240 · doi:10.1007/s00707-011-0608-6
[6] Andrianov IV, Danishevs’kyy VV, Weichert D (2002) Asymptotic determination of effective elastic properties of composite materials with fibrous square-shaped inclusions. Eur J Mech A/Solids 21(6):1019-1036 · Zbl 1027.74055 · doi:10.1016/S0997-7538(02)01250-0
[7] Arnold DN, Awanou G (2011) The serendipity family of finite elements. Found Comput Math 11(3):337-344 · Zbl 1218.65125 · doi:10.1007/s10208-011-9087-3
[8] Babuska I, Melenk JM (1997) The partition of unity method. Int J Numer Methods Eng 40(4):727-758 · Zbl 0949.65117 · doi:10.1002/(SICI)1097-0207(19970228)40:4<727::AID-NME86>3.0.CO;2-N
[9] Babuska I, Nobile F, Tempone R (2007) A stochastic collocation method for elliptic partial differential equations with random input data. SIAM J Numer Anal 45(3):1005-1034 · Zbl 1151.65008 · doi:10.1137/050645142
[10] Belytschko T, Gracie R, Ventura G (2009) A review of extended/generalized finite element methods for material modeling. Modell Simul Mater Sci Eng 17(4):043,001 · doi:10.1088/0965-0393/17/4/043001
[11] Castaneda PP, Galipeau E (2011) Homogenization-based constitutive models for magnetorheological elastomers at finite strain. J Mech Phys Solids 59(2):194-215 · Zbl 1270.74075 · doi:10.1016/j.jmps.2010.11.004
[12] Chatzigeorgiou G, Javili A, Steinmann P (2013) Unified magnetomechanical homogenization framework with application to magnetorheological elastomers. Math Mech Solids 2012:193-211 · Zbl 1355.74065
[13] Chevreuil M, Nouy A, Safatly E (2013) A multiscale method with patch for the solution of stochastic partial differential equations with localized uncertainties. Comput Methods Appl Mech Eng 255:255-274 · Zbl 1297.65192 · doi:10.1016/j.cma.2012.12.003
[14] Cottereau R (2013) A stochastic-deterministic coupling method for multiscale problems. Application to numerical homogenization of random materials. In Procedia IUTAM. IUTAM Symposium on Multiscale Problems in Stochastic Mechanics, vol. 6, pp 35-43 · Zbl 1273.65012
[15] Cottereau R, Clouteau D, Ben Dhia H (2011) Localized modeling of uncertainty in the arlequin framework. In: Belyaev AK, Langley RS (eds) IUTAM Symposium on the Vibration Analysis of Structures with Uncertainties, IUTAM Bookseries. Springer, Netherlands, pp 457-468
[16] Deb MK, Babuska IM, Oden J (2001) Solution of stochastic partial differential equations using galerkin finite element techniques. Comput Methods Appl Mech Eng 190(48):6359-6372 · Zbl 1075.65006 · doi:10.1016/S0045-7825(01)00237-7
[17] Dimas LS, Giesa T, Buehler MJ (2014) Coupled continuum and discrete analysis of random heterogeneous materials: elasticity and fracture. J Mech Phys Solids 63:481-490 · doi:10.1016/j.jmps.2013.07.006
[18] Dolbow J, Moes N, Belytschko, T.: Discontinuous enrichment in finite elements with a partition of unity method. Finite Elements in Analysis and Design 36, 235-260, (2000) Robert J. Melosh Medal Competition, Duke University, Durham NC, USA, March 1999 · Zbl 0981.74057
[19] Ernst O, Powell C, Silvester D, Ullmann E (2009) Efficient solvers for a linear stochastic galerkin mixed formulation of diffusion problems with random data. SIAM J Sci Comput 31(2):1424-1447 · Zbl 1187.35298 · doi:10.1137/070705817
[20] Ernst OG, Mugler A, Starkloff HJ, Ullmann E (2012) On the convergence of generalized polynomial chaos expansions. ESAIM Math Model Numer Anal 46:317-339 · Zbl 1273.65012 · doi:10.1051/m2an/2011045
[21] Ernst OG, Ullmann E (2010) Stochastic galerkin matrices. SIAM J Matrix Anal Appl 31(4):1848-1872 · Zbl 1205.65021 · doi:10.1137/080742282
[22] Fries TP, Belytschko T (2010) The extended/generalized finite element method: An overview of the method and its applications. Int J Numer Methods Eng 84(3):253-304 · Zbl 1202.74169
[23] Galipeau E, Castaneda PP (2012) The effect of particle shape and distribution on the macroscopic behavior of magnetoelastic composites. Int J Solids Struct 49(1):1-17 · doi:10.1016/j.ijsolstr.2011.08.014
[24] Galipeau E, Castaneda PP (2013) A finite-strain constitutive model for magnetorheological elastomers: magnetic torques and fiber rotations. J Mech Phys Solids 61(4):1065-1090 · doi:10.1016/j.jmps.2012.11.007
[25] Galipeau E, Rudykh S, deBotton G, Castaneda PP (2014) Magnetoactive elastomers with periodic and random microstructures. Int J Solids Struct 51(18):3012-3024 · doi:10.1016/j.ijsolstr.2014.04.013
[26] Ghanem RG, Spanos PD (2003) Stochastic finite elements: a spectral approach. Dover Publications, inc, New York · Zbl 0953.74608
[27] Hadigol M, Doostan A, Matthies HG, Niekamp R (2014) Partitioned treatment of uncertainty in coupled domain problems: a separated representation approach. Comput Methods Appl Mech Eng 274:103-124 · Zbl 1296.65158 · doi:10.1016/j.cma.2014.02.004
[28] Hammer PC, Stroud AH (1956) Numerical integration over simplexes. Math Tables Other Aids to Comput 10:137-139 · Zbl 0070.35405 · doi:10.2307/2002484
[29] Hiriyur B, Waisman H, Deodatis G (2011) Uncertainty quantification in homogenization of heterogeneous microstructures modeled by xfem. Int J Numer Methods Eng 88(3):257-278 · Zbl 1242.74125 · doi:10.1002/nme.3174
[30] Hughes T (2012) The finite element method: linear static and dynamic finite element analysis. Dover civil and mechanical engineering. Dover publications, Mineola
[31] Javili A, Chatzigeorgiou G, Steinmann P (2013) Computational homogenization in magneto-mechanics. Int J Solids Struct 50(25-26):4197-4216 · doi:10.1016/j.ijsolstr.2013.08.024
[32] Khoromskij B, Litvinenko A, Matthies H (2009) Application of hierarchical matrices for computing the karhunen-loeve expansion. Computing 84(1-2):49-67 · Zbl 1162.65306 · doi:10.1007/s00607-008-0018-3
[33] Kovetz A (2000) Electromagnetic Theory. Oxford science publications. Oxford University Press, Oxford · Zbl 1038.78001
[34] Kucerova A, Sykora J, Rosic B, Matthies HG (2012) Acceleration of uncertainty updating in the description of transport processes in heterogeneous materials. J Comput Appl Math 236(18), 4862 - 4872. In FEMTEC 2011: 3rd international conference on computational methods in engineering and science, May 9-13, 2011 · Zbl 1426.76633
[35] Lang C, Doostan A, Maute K (2012) Extended stochastic fem for diffusion problems with uncertain material interfaces. Comput Mech 51(6):1031-1049 · Zbl 1366.74071 · doi:10.1007/s00466-012-0785-8
[36] Lang C, Sharma A, Doostan A, Maute K (2015) Heaviside enriched extended stochastic fem for problems with uncertain material interfaces. Comput Mech 56(5):753-767 · Zbl 1329.74279 · doi:10.1007/s00466-015-1199-1
[37] Leclerc W, Karamian-Surville P, Vivet A (2013) An efficient stochastic and double-scale model to evaluate the effective elastic properties of 2d overlapping random fibre composites. Comput Mater Sci 69:481-493 · doi:10.1016/j.commatsci.2012.10.036
[38] Legrain G, Cartraud P, Perreard I, Moes N (2011) An x-fem and level set computational approach for image-based modelling: application to homogenization. Int J Numer Methods Eng 86(7):915-934 · Zbl 1235.74297 · doi:10.1002/nme.3085
[39] Lucas V, Golinval JC, Paquay S, Nguyen VD, Noels L, Wu L (2015) A stochastic computational multiscale approach; application to MEMS resonators. Comput Methods Appl Mech Eng 294:141-167 · Zbl 1423.74198 · doi:10.1016/j.cma.2015.05.019
[40] Ma J, Sahraee S, Wriggers P, De Lorenzis L (2015) Stochastic multiscale homogenization analysis of heterogeneous materials under finite deformations with full uncertainty in the microstructure. Comput Mech 55(5):819-835 · Zbl 1329.74243 · doi:10.1007/s00466-015-1136-3
[41] Ma J, Zhang J, Li L, Wriggers P, Sahraee S (2014) Random homogenization analysis for heterogeneous materials with full randomness and correlation in microstructure based on finite element method and monte-carlo method. Comput Mech 54(6):1395-1414 · Zbl 1309.74063 · doi:10.1007/s00466-014-1065-6
[42] Melenk J, Babuska I (1996) The partition of unity finite element method: basic theory and applications. Comput Methods Appl Mech Eng 139:289-314 · Zbl 0881.65099 · doi:10.1016/S0045-7825(96)01087-0
[43] Moes N, Cloirec M, Cartraud P, Remacle JF (2003) A computational approach to handle complex microstructure geometries. Comput Methods Appl Mech Eng 192(2830):3163-3177 Multiscale Computational Mechanics for Materials and Structures · Zbl 1054.74056 · doi:10.1016/S0045-7825(03)00346-3
[44] Moes N, Dolbow J, Belytschko T (1999) A finite element method for crack growth without remeshing. International Journal for Numerical Methods in Engineering 46(1):131-150 · Zbl 0955.74066 · doi:10.1002/(SICI)1097-0207(19990910)46:1<131::AID-NME726>3.0.CO;2-J
[45] Nouy A, Clement A (2010) Extended stochastic finite element method for the numerical simulation of heterogeneous materials with random material interfaces. Int J Numer Methods Eng 83(10):1312-1344 · Zbl 1202.74182 · doi:10.1002/nme.2865
[46] Nouy A, Clement A, Schoefs F, Moes N (2008) An extended stochastic finite element method for solving stochastic partial differential equations on random domains. Comput Methods Appl Mech Eng 197(51-52):4663-4682 · Zbl 1194.74457 · doi:10.1016/j.cma.2008.06.010
[47] Pajonk O, Rosic BV, Matthies HG (2013) Sampling-free linear bayesian updating of model state and parameters using a square root approach. Comput Geosci 55:70-83 Ensemble Kalman filter for data assimilation · doi:10.1016/j.cageo.2012.05.017
[48] Papoulis A, Pillai SU (2001) Probability, random variables and stochastic processes. McGraw-Hill Education, New York
[49] Pivovarov D, Steinmann P (2016) Modified sfem for computational homogenization of heterogeneous materials with microstructural geometric uncertainties. Comput Mech 57(1):123-147 · Zbl 1381.74179 · doi:10.1007/s00466-015-1224-4
[50] Rosic B, Matthies H (2008) Computational approaches to inelastic media with uncertain parameters. J Serbian Soc Comput Mech 2(1):28-43
[51] Rosic B, Matthies H, Zivkovic M (2011) Uncertainty quantification of inifinitesimal elastoplasticity. Sci Tech Rev 61(2):3-9
[52] Rosic B, Matthies HG (2011) Plasticity described by uncertain parameters: A variational inequality approach. In: Proceedings of XI International Conference on Computational Plasticity, Fundamentals and Applications (COMPLAS), pp. 385-395 · Zbl 1369.65151
[53] Rosic BV (2012) Variational formulations and functional approximation algorithms in stochastic plasticity of materials. Ph.D. thesis, Faculty of Engineering , Kragujevac
[54] Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia · Zbl 1031.65046 · doi:10.1137/1.9780898718003
[55] Sakata S, Ashida F (2011) Hierarchical stochastic homogenization analysis of a particle reinforced composite material considering non-uniform distribution of microscopic random quantities. Comput Mech 48(5):529-540 · Zbl 1384.74040 · doi:10.1007/s00466-011-0604-7
[56] Sakata S, Ashida F, Enya K (2012) 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. Comput Mater Sci 62:35-46 · doi:10.1016/j.commatsci.2012.05.008
[57] Sakata S, Ashida F, Kojima T (2008) Stochastic homogenization analysis on elastic properties of fiber reinforced composites using the equivalent inclusion method and perturbation method. Int J Solids Struct 45(2526):6553-6565 · Zbl 1168.74423 · doi:10.1016/j.ijsolstr.2008.08.017
[58] Sakata S, Ashida F, Zako M (2008) Kriging-based approximate stochastic homogenization analysis for composite materials. Comput Methods Appl Mech Eng 197(2124):1953-1964 · Zbl 1194.74284 · doi:10.1016/j.cma.2007.12.011
[59] Savvas D, Stefanou G, Papadrakakis M, Deodatis G (2014) Homogenization of random heterogeneous media with inclusions of arbitrary shape modeled by xfem. Comput Mech 54(5):1221-1235 · Zbl 1311.74008 · doi:10.1007/s00466-014-1053-x
[60] Shynk JJ (2012) Probability, random variables, and random processes: theory and signal processing applications. Wiley-Interscience, Hoboken · Zbl 1275.60002
[61] Spieler C, Kaestner M, Goldmann J, Brummund J, Ulbricht V (2013) Xfem modeling and homogenization of magnetoactive composites. Acta Mech 224(11):2453-2469 · Zbl 1398.74406 · doi:10.1007/s00707-013-0948-5
[62] Stefanou G (2009) The stochastic finite element method: past, present and future. Comput Methods Appl Mech Eng 198:1031-1051 · Zbl 1229.74140 · doi:10.1016/j.cma.2008.11.007
[63] Stefanou G (2014) Simulation of heterogeneous two-phase media using random fields and level sets. Front Struct Civil Eng 9(2):114-120 · doi:10.1007/s11709-014-0267-5
[64] Stefanou G, Nouy A, Clement A (2009) Identification of random shapes from images through polynomial chaos expansion of random level set functions. Int J Numer Methods Eng 79(2):127-155 · Zbl 1171.74449 · doi:10.1002/nme.2546
[65] Stefanou G, Papadrakakis M (2004) Stochastic finite element analysis of shells with combined random material and geometric properties. Comput Methods Appl Mech Eng 193:139-160 · Zbl 1075.74681 · doi:10.1016/j.cma.2003.10.001
[66] Strouboulis T, Babuska I, Copps K (2000) The design and analysis of the generalized finite element method. Comput Methods Appl Mech Eng 181:43-69 · Zbl 0983.65127 · doi:10.1016/S0045-7825(99)00072-9
[67] Stroud A (1969) A fifth degree integration formula for the n-simplex. SIAM J Num Anal 6:90-98 · Zbl 0176.46601 · doi:10.1137/0706009
[68] Stroud AH (1976) Some fourth degree integration formulas for simplexes. Math Comput 30(134):291-294 · Zbl 0326.65023 · doi:10.1090/S0025-5718-1976-0391484-0
[69] Ullmann E, Elman HC, Ernst OG (2012) Efficient iterative solvers for stochastic galerkin discretizations of log-transformed random diffusion problems. SIAM J Sci Comput 34(2):659-682 · Zbl 1251.35200 · doi:10.1137/110836675
[70] Vondrejc J, Zeman J, Marek I (2012) Large-Scale Scientific Computing: 8th International Conference, LSSC 2011, Sozopol, Bulgaria, June 6-10, 2011, Revised Selected Papers, chap. Analysis of a Fast Fourier Transform Based Method for Modeling of Heterogeneous Materials, pp. 515-522. Springer Berlin Heidelberg, Berlin, Heidelberg · Zbl 1354.74235
[71] Vondrejc J, Zeman J, Marek I (2014) An fft-based galerkin method for homogenization of periodic media. Comput Math Appl 68(3):156-173 · Zbl 1369.65151 · doi:10.1016/j.camwa.2014.05.014
[72] Xu XF (2007) A multiscale stochastic finite element method on elliptic problems involving uncertainties. Comput Methods Appl Mech Eng 196:2723-2736 · Zbl 1173.74449 · doi:10.1016/j.cma.2007.02.002
[73] Zaccardi C, Chamoin L, Cottereau R, Ben Dhia H (2013) Error estimation and model adaptation for a stochastic-deterministic coupling method based on the arlequin framework. Int J Numer Methods Eng 96(2):87-109 · Zbl 1352.65635 · doi:10.1002/nme.4540
[74] Zienkiewicz O (1971) The finite element method in engineering science. McGraw-Hill, New York · Zbl 0237.73071
[75] Zohdi T, Feucht M, Gross D, Wriggers P (1998) A description of macroscopic damage through microstructural relaxation. Int J Numer Methods Eng 43(3):493-506 · Zbl 0948.74004 · doi:10.1002/(SICI)1097-0207(19981015)43:3<493::AID-NME461>3.0.CO;2-N
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.