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dc.contributor.author | Llopis-Albert, Carlos | es_ES |
dc.contributor.author | Rubio Montoya, Francisco José | es_ES |
dc.contributor.author | Valero Chuliá, Francisco José | es_ES |
dc.contributor.author | Liao, Hunchang | es_ES |
dc.contributor.author | Zeng, Shouzhen | es_ES |
dc.date.accessioned | 2021-01-19T04:32:21Z | |
dc.date.available | 2021-01-19T04:32:21Z | |
dc.date.issued | 2019-11 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/159352 | |
dc.description.abstract | [EN] The micro and meso-structural characteristics of materials present an inherent variability because of the intrinsic scatter in raw material and manufacturing processes. This problem is exacerbated in highly heterogeneous materials, which shows significant uncertainties in the macroscale material properties. Therefore, providing optimal designs and reliable structural analyses strongly depend on the selection of the underlying material property models. This paper is intended to provide insight into such a dependence by means of a stochastic inverse model based on an iterative optimization process depending only of one parameter, thus avoiding complex parametrizations. It relies on nonlinear combinations of material property realizations with a defined spatial structure for constraining stochastic simulations to data within the framework of a Finite Element approach. In this way, the procedure gradually deforms unconditional material property realizations to approximate the reproduction of information including mechanical parameters (such as Young's modulus and Poisson's ratio fields) and variables (e.g., stress and strain fields). It allows dealing with non-multiGaussian structures for the spatial structure of the material property realizations, thus allowing to reproduce the coalescence and connectivity among phases and existing crack patterns that often take place in composite materials, being these features crucial in order to obtain more reliable safety factors and fatigue life predictions. The methodology has been successfully applied for the characterization of a complex case study, where an uncertainty assessment has been carried out by means of multiple equally likely realizations. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IOP Publishing | es_ES |
dc.relation.ispartof | Materials Research Express | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Heterogeneity | es_ES |
dc.subject | Uncertainty | es_ES |
dc.subject | Composite materials | es_ES |
dc.subject | Finite element method | es_ES |
dc.subject | Inverse modeling | es_ES |
dc.subject.classification | INGENIERIA MECANICA | es_ES |
dc.title | Stochastic inverse finite element modeling for characterization of heterogeneous material properties | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1088/2053-1591/ab4c72 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials | es_ES |
dc.description.bibliographicCitation | Llopis-Albert, C.; Rubio Montoya, FJ.; Valero Chuliá, FJ.; Liao, H.; Zeng, S. (2019). Stochastic inverse finite element modeling for characterization of heterogeneous material properties. Materials Research Express. 6(11):1-16. https://doi.org/10.1088/2053-1591/ab4c72 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1088/2053-1591/ab4c72 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 6 | es_ES |
dc.description.issue | 11 | es_ES |
dc.identifier.eissn | 2053-1591 | es_ES |
dc.relation.pasarela | S\395395 | es_ES |
dc.description.references | Albanesi, A., Bre, F., Fachinotti, V., & Gebhardt, C. (2018). Simultaneous ply-order, ply-number and ply-drop optimization of laminate wind turbine blades using the inverse finite element method. Composite Structures, 184, 894-903. doi:10.1016/j.compstruct.2017.10.051 | es_ES |
dc.description.references | Albanesi, A., Fachinotti, V., Peralta, I., Storti, B., & Gebhardt, C. (2017). Application of the inverse finite element method to design wind turbine blades. Composite Structures, 161, 160-172. doi:10.1016/j.compstruct.2016.11.039 | es_ES |
dc.description.references | Borkowski, L., & Kumar, R. S. (2018). Inverse method for estimation of composite kink-band toughness from open-hole compression strength data. Composite Structures, 186, 183-192. doi:10.1016/j.compstruct.2017.12.006 | es_ES |
dc.description.references | Baby, A., Nayak, S. Y., Heckadka, S. S., Purohit, S., Bhagat, K. K., & Thomas, L. G. (2019). Mechanical and morphological characterization of carbonized egg-shell fillers/Borassus fibre reinforced polyester hybrid composites. Materials Research Express, 6(10), 105342. doi:10.1088/2053-1591/ab3bb7 | es_ES |
dc.description.references | Borovinšek, M., Vesenjak, M., & Ren, Z. (2016). Estimating the base material properties of sintered metallic hollow spheres by inverse engineering procedure. Mechanics of Materials, 100, 22-30. doi:10.1016/j.mechmat.2016.06.001 | es_ES |
dc.description.references | Capilla, J. E., & Llopis-Albert, C. (2009). Gradual conditioning of non-Gaussian transmissivity fields to flow and mass transport data: 1. Theory. Journal of Hydrology, 371(1-4), 66-74. doi:10.1016/j.jhydrol.2009.03.015 | es_ES |
dc.description.references | Charmpis, D. C., Schuëller, G. I., & Pellissetti, M. F. (2007). The need for linking micromechanics of materials with stochastic finite elements: A challenge for materials science. Computational Materials Science, 41(1), 27-37. doi:10.1016/j.commatsci.2007.02.014 | es_ES |
dc.description.references | Cooreman, S., Lecompte, D., Sol, H., Vantomme, J., & Debruyne, D. (2007). Identification of Mechanical Material Behavior Through Inverse Modeling and DIC. Experimental Mechanics, 48(4), 421-433. doi:10.1007/s11340-007-9094-0 | es_ES |
dc.description.references | Goodarzi, A., Fotouhi, M., & Shodja, H. M. (2016). Inverse scattering problem of reconstruction of an embedded micro-/nano-size scatterer within couple stress theory with micro inertia. Mechanics of Materials, 103, 123-134. doi:10.1016/j.mechmat.2016.09.011 | es_ES |
dc.description.references | Herrera-Solaz, V., Segurado, J., & LLorca, J. (2015). On the robustness of an inverse optimization approach based on the Levenberg–Marquardt method for the mechanical behavior of polycrystals. European Journal of Mechanics - A/Solids, 53, 220-228. doi:10.1016/j.euromechsol.2015.05.005 | es_ES |
dc.description.references | Hu, L. Y. (2000). Mathematical Geology, 32(1), 87-108. doi:10.1023/a:1007506918588 | es_ES |
dc.description.references | Ignacio, I. (2014). Different Ways to Consider Heterogeneity in Quase-fragile Materials Using a Version of Lattice Model. Procedia Materials Science, 3, 499-504. doi:10.1016/j.mspro.2014.06.083 | es_ES |
dc.description.references | Kashfi, M., Majzoobi, G. H., Bonora, N., Iannitti, G., Ruggiero, A., & Khademi, E. (2019). A new overall nonlinear damage model for fiber metal laminates based on continuum damage mechanics. Engineering Fracture Mechanics, 206, 21-33. doi:10.1016/j.engfracmech.2018.11.043 | es_ES |
dc.description.references | Kashfi, M., Majzoobi, G. H., Bonora, N., Iannitti, G., Ruggiero, A., & Khademi, E. (2017). A study on fiber metal laminates by using a new damage model for composite layer. International Journal of Mechanical Sciences, 131-132, 75-80. doi:10.1016/j.ijmecsci.2017.06.045 | es_ES |
dc.description.references | Kim, H., Kim, D., Ahn, K., Yoo, D., Son, H.-S., Kim, G.-S., & Chung, K. (2015). Inverse characterization method for mechanical properties of strain/strain-rate/temperature/temperature-history dependent steel sheets and its application for hot press forming. Metals and Materials International, 21(5), 874-890. doi:10.1007/s12540-015-5141-z | es_ES |
dc.description.references | Kouznetsova, V., Brekelmans, W. A. M., & Baaijens, F. P. T. (2001). An approach to micro-macro modeling of heterogeneous materials. Computational Mechanics, 27(1), 37-48. doi:10.1007/s004660000212 | es_ES |
dc.description.references | Li, G., Xu, F., Sun, G., & Li, Q. (2014). Identification of mechanical properties of the weld line by combining 3D digital image correlation with inverse modeling procedure. The International Journal of Advanced Manufacturing Technology, 74(5-8), 893-905. doi:10.1007/s00170-014-6034-x | es_ES |
dc.description.references | Libanori, R., Erb, R. M., Reiser, A., Le Ferrand, H., Süess, M. J., Spolenak, R., & Studart, A. R. (2012). Stretchable heterogeneous composites with extreme mechanical gradients. Nature Communications, 3(1). doi:10.1038/ncomms2281 | es_ES |
dc.description.references | Lloyd, A. A., Wang, Z. X., & Donnelly, E. (2015). Multiscale Contribution of Bone Tissue Material Property Heterogeneity to Trabecular Bone Mechanical Behavior. Journal of Biomechanical Engineering, 137(1). doi:10.1115/1.4029046 | es_ES |
dc.description.references | Mehrez, L., Moens, D., & Vandepitte, D. (2012). Stochastic identification of composite material properties from limited experimental databases, part I: Experimental database construction. Mechanical Systems and Signal Processing, 27, 471-483. doi:10.1016/j.ymssp.2011.09.004 | es_ES |
dc.description.references | Mikdam, A., Makradi, A., Koutsawa, Y., & Belouettar, S. (2013). Microstructure effect on the mechanical properties of heterogeneous composite materials. Composites Part B: Engineering, 44(1), 714-721. doi:10.1016/j.compositesb.2012.01.081 | es_ES |
dc.description.references | Mortazavi, F., Ghossein, E., Lévesque, M., & Villemure, I. (2014). High resolution measurement of internal full-field displacements and strains using global spectral digital volume correlation. Optics and Lasers in Engineering, 55, 44-52. doi:10.1016/j.optlaseng.2013.10.007 | es_ES |
dc.description.references | Ni, Y., & Chiang, M. Y. M. (2007). Prediction of elastic properties of heterogeneous materials with complex microstructures. Journal of the Mechanics and Physics of Solids, 55(3), 517-532. doi:10.1016/j.jmps.2006.09.001 | es_ES |
dc.description.references | Pitangueira, R. L., & Silva, R. R. e. (2002). Numerical Characterization of Concrete Heterogeneity. Materials Research, 5(3), 309-314. doi:10.1590/s1516-14392002000300015 | es_ES |
dc.description.references | Oller, S., Miquel Canet, J., & Zalamea, F. (2005). Composite Material Behavior Using a Homogenization Double Scale Method. Journal of Engineering Mechanics, 131(1), 65-79. doi:10.1061/(asce)0733-9399(2005)131:1(65) | es_ES |
dc.description.references | Pottier, T., Toussaint, F., & Vacher, P. (2011). Contribution of heterogeneous strain field measurements and boundary conditions modelling in inverse identification of material parameters. European Journal of Mechanics - A/Solids, 30(3), 373-382. doi:10.1016/j.euromechsol.2010.10.001 | es_ES |
dc.description.references | Rahmani, B., Mortazavi, F., Villemure, I., & Levesque, M. (2013). A new approach to inverse identification of mechanical properties of composite materials: Regularized model updating. Composite Structures, 105, 116-125. doi:10.1016/j.compstruct.2013.04.025 | es_ES |
dc.description.references | The Mechanics of Constitutive Modeling. (2005). doi:10.1016/b978-0-08-044606-6.x5000-0 | es_ES |
dc.description.references | Sakata, S., Ashida, F., & Zako, M. (2008). Kriging-based approximate stochastic homogenization analysis for composite materials. Computer Methods in Applied Mechanics and Engineering, 197(21-24), 1953-1964. doi:10.1016/j.cma.2007.12.011 | es_ES |
dc.description.references | Samavati, N., McGrath, D. M., Jewett, M. A. S., van der Kwast, T., Ménard, C., & Brock, K. K. (2014). Effect of material property heterogeneity on biomechanical modeling of prostate under deformation. Physics in Medicine and Biology, 60(1), 195-209. doi:10.1088/0031-9155/60/1/195 | es_ES |
dc.description.references | Sanchez-Palencia, E., & Zaoui, A. (Eds.). (1987). Homogenization Techniques for Composite Media. Lecture Notes in Physics. doi:10.1007/3-540-17616-0 | es_ES |
dc.description.references | Sharifi, H., & Larouche, D. (2014). Numerical Study of Variation of Mechanical Properties of a Binary Aluminum Alloy with Respect to Its Grain Shapes. Materials, 7(4), 3065-3083. doi:10.3390/ma7043065 | es_ES |
dc.description.references | Sriramula, S., & Chryssanthopoulos, M. K. (2009). Quantification of uncertainty modelling in stochastic analysis of FRP composites. Composites Part A: Applied Science and Manufacturing, 40(11), 1673-1684. doi:10.1016/j.compositesa.2009.08.020 | es_ES |
dc.description.references | Torquato, S. (2010). Optimal Design of Heterogeneous Materials. Annual Review of Materials Research, 40(1), 101-129. doi:10.1146/annurev-matsci-070909-104517 | es_ES |
dc.description.references | Wu, X., & Zhu, Y. (2017). Heterogeneous materials: a new class of materials with unprecedented mechanical properties. Materials Research Letters, 5(8), 527-532. doi:10.1080/21663831.2017.1343208 | es_ES |
dc.description.references | Zhang, Z., Zhan, C., Shankar, K., Morozov, E. V., Singh, H. K., & Ray, T. (2017). Sensitivity analysis of inverse algorithms for damage detection in composites. Composite Structures, 176, 844-859. doi:10.1016/j.compstruct.2017.06.019 | es_ES |
dc.description.references | Zottis, J., Soares Diehl, C. A. T., & Rocha, A. da S. (2018). Evaluation of experimentally observed asymmetric distributions of hardness, strain and residual stress in cold drawn bars by FEM-simulation. Journal of Materials Research and Technology, 7(4), 469-478. doi:10.1016/j.jmrt.2018.01.004 | es_ES |