ORIGINAL PAPER
Identification of Local Elastic Parameters in Heterogeneous Materials Using a Parallelized Femu Method
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Photomechanics & Experimental Mechanics (PEM) team, Dept. Génie Mécanique et Systèmes Complexes (GMSC), Institut P’ UPR 3346 CNRS - , Université de Poitiers – ENSMA, SP2MI – Téléport 2, 11 Boulevard Marie et Pierre Curie, BP 30179, F86962 Futuroscope Chasseneuil Cedex, France
 
 
Online publication date: 2019-12-04
 
 
Publication date: 2019-12-01
 
 
International Journal of Applied Mechanics and Engineering 2019;24(4):140-156
 
KEYWORDS
ABSTRACT
In this work, we explore the possibilities of the widespread Finite Element Model Updating method (FEMU) in order to identify the local elastic mechanical properties in heterogeneous materials. The objective function is defined as a quadratic error of the discrepancy between measured fields and simulated ones. We compare two different formulations of the function, one based on the displacement fields and one based on the strain fields. We use a genetic algorithm in order to minimize these functions. We prove that the strain functional associated with the genetic algorithm is the best combination. We then improve the implementation of the method by parallelizing the algorithm in order to reduce the computation cost. We validate the approach with simulated cases in 2D.
REFERENCES (23)
1.
Merzouki T., Nouri H and Roger F. (2014): Direct identification of nonlinear damage behavior of composite materials using the constitutive equation gap method. – International Journal of Mechanical Sciences, vol.89, pp.487-499.
 
2.
Passieux J.-C., Bugarin F., David C., Périé J.-N. and Robert L. (2014): Multiscale displacement field measurement using digital image correlation: application to the identification of elastic properties. – Experimental Mechanics, vol.55, No.1, pp.121-137.
 
3.
Azzouna M.B., Feissel P. and Villon P. (2015): Robust identification of elastic properties using the modified constitutive relation error. – Computer Methods in Applied Mechanics and Engineering, vol.295, pp.196-218.
 
4.
He T., Liu L. and Makeev A. (2018): Uncertainty analysis in composite material properties characterization using digital image correlation and finite element model updating. – Composite Structures, vol.184, pp.337-351.
 
5.
Grédiac M. (1989): Principe des travaux virtuels et identification. – Comptes Rendus de l’Académie des Sciences, vol.309, pp.1-5.
 
6.
Geymonat G., Hild F. and Pagano S. (2002): Identification of elastic parameters by displacement field measurement. – Comptes Rendus Mécanique, vol.330, No.6, pp.403-408.
 
7.
Calderon A. (1980): On an inverse boundary value problem. – Seminar on Numerical Analysis and its Applications to Continuum Physics, Brazilian Mathematical Society.
 
8.
Claire D., Hild F. and Roux S. (2004): A finite element formulation to identify damage fields: the equilibrium gap method. – International Journal for Numerical Methods in Engineering, vol.61, No.2, pp.189-208.
 
9.
Kavanagh K.T. and Clough R.W. (1971): Finite element applications in the characterization of elastic solids. – International Journal of Solids and Structures, vol.7, No.1, pp.11-23.
 
10.
Roux S. and Hild F. (2018): Optimal procedure for the identification of constitutive parameters from experimentally measured displacement fields. – International Journal of Solids and Structures, https://doi.org/10.1016/j.ijso....
 
11.
Martins J.M.P., Andrade-Campos A. and Thuillier S. (2018) : Comparison of inverse identification strategies for constitutive mechanical models using full-field measurements. – Internation Journal of Mechanical Science, vol.145, pp.330-345.
 
12.
Battaglia G., Di Matteo A., Micale G. and Pirrotta A. (2018): Vibration-based identification of mechanical properties of orthotropic arbitrarily shaped plates : Numerical and experimental assessment. – Composites Part B, vol.150, pp.212-225.
 
13.
Nguyen T.T., Huntley J.M., Ashcroft I.A., Ruiz P.D. and Pierron F. (2014): A Fourier-series-based Virtual Fields Method for the identification of 2-D stiffness and traction distributions – Strains, vol.50, No.5, pp.454-468.
 
14.
Lubineau G., Moussawi A., Xu J. and Gras R. (2015): A domain decomposition approach for full-field measurements based identification of local elastic parameters. – International Journal of Solides and Structures, vol.55, pp.44-57.
 
15.
Fernandez J.R., Lopez-Campos J.A., Segade A. and Vilan J.A. (2018): A genetic algorithm for the characterization of hyperelastic materials. – Applied Mathematics and Computation, vol.329, pp.239-250.
 
16.
Goldberg D. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning. – Addison-Wesley Professional.
 
17.
Back T., Fogel D.B. and Michalewicz Z. (1997): Handbook of Evolutionary Computation (Computation Intelligence Library). – 1st. Oxford University Press.
 
18.
Michalewicz Z. (1996): Genetic Algorithms+Data Structures = Evolution Programs. – Springer Berlin Heidelberg.
 
19.
Umbarkar A.J. and Sheth P.D. (2015): Crossover operators in genetic algorithms: a review. – ICTACT Journal on Soft Computing, vol.6, No.1, pp.1083-1092.
 
20.
Cantu-Paz E. (1998): A survey of parallel genetic algorithms. – Calculateurs parallèles, Réseaux et Systèmes Répartis, vol.10, pp.141-171.
 
21.
Gropp W., Lusk E. and Skjellum A. (2014): Using MPI: Portable Parallel Programming with the Message-Passing Interface (Scientific and Engineering Computation). – The MIT Press.
 
22.
Cast3M-CEA (2001) - http://www.cast3m.cea.fr.
 
23.
Oura P., Fraga B., Lopez-Novoa U. and Stoesser T. (2019): Scalability of an Eulerian-Lagrangian large-eddy simulation solver with hybrid MPI/OpenMP parallelization. – Computers and Fluids, vol.179, pp.123-126.
 
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ISSN:1734-4492
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