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Advances in Computational Materials Micro-Mechanics
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Advances in Computational Materials Micro-Mechanics

A special issue of Materials (ISSN 1996-1944).

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 8428

Special Issue Editors

Prof. Dr. Ivano Benedetti

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, Viale delle Scienze, Edificio 8, 90128 Palermo, Italy
Interests: computational mechanics; multiscale materials modelling; composite and heterogenous materials; computational homogenization; damage and fracture mechanics
Prof. Fabrice Barbe

E-Mail Website
Guest Editor
Group of Physics of Materials, INSA Rouen Normandie, France
Interests: microstructure-based modelling; full-field multiscale modelling; polycrystals plasticity; complex microstructures
Dr. Antonio Caggiano

E-Mail Website
Guest Editor
Department of Civil, Chemical and Environmental Engineering, University of Genova, Genova, Italy
Interests: sustainability in construction and building materials; recycling; smart materials; smart buildings; energy-saving; green buildings; eco-friendly materials; nearly zero-energy buildings; energy efficiency; energy storage; phase change materials; renewable energy resources; zero CO2 emissions; CO2 storage in materials; modeling; multiscale; multiphysics; micro- and meso-scale
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, progress in the micro-/nano-characterization of experimental materials and the increased affordability of high-performance computing (HPC) have favoured the development of extensive research in computational materials micromechanics, which now effectively supports the development of novel high-performance materials for multifunctional engineering applications.

Investigations in this field focus on elucidating the tight relationships between material microstructures, the micro-mechanisms governing their behaviour, and their effective macroscopic properties in real service conditions. The interest in characterizing the structure–property relationships has recently been extended to the consideration of manufacturing processes and parameters as valuable input elements to be included in the process of materials development: conditions for a top-down approach to the design of advanced materials and components, where process parameters can be directly related to the desired properties, can then be envisaged.

In many of the studies in this field, microstructural analyses are based on the explicit full-scale simulation of the micro-mechanical behaviour of the material constituents (fibres, inclusions, and matrix in composite materials; individual crystals in polycrystalline materials; aggregates in cementitious materials, etc.) and of the interfaces between such entities, which often constitute the source of complex non-linear physical phenomena.

The computational modelling of the microscale requires detailed multi-physics description of the material constituents. This poses formidable challenges from the modelling point of view, ranging from the inclusion of adequate constitutive descriptions to the selection of the most suitable numerical method for effective simulation. Further complexity arises when the study of highly non-linear phenomena (e.g., micro-damage and micro-cracking, phase transformations) are of interest. The analysis of such aspects often requires the development of ad-hoc computational strategies able to address potential issues (e.g., damage localization, mesh-dependency) while ensuring robustness and predictive ability. Reliable micro-structural analyses are often the enabling item for the development of effective multi-scale methods.

This Special Issue is aimed at exploring recent advances in the above-described and rapidly evolving multi-disciplinary field. Contributions focused on elucidating, through modelling, physical aspects of materials behaviour or addressing the development of new mathematical/computational techniques for effective materials modelling are invited. Articles related to the application of emerging methodologies/technologies to materials computational micro-mechanics (virtual element method, phase-field modelling, artificial intelligence, machine learning and surrogate modelling, etc.) are welcome, as are contributions describing the use of computational methods in the design of novel high-performance materials for advanced engineering applications.

The Guest Editors of this Special Issue are also leading a mini-symposium entitled Advances in Computational Materials Micro-Mechanics, to be held at the next ICCES 2020 (26th International Conference on Computational & Experimental Engineering and Sciences) - http://www.iccesconf.org/, Budva, Montenegro/26–30 April 2020. Contributions to this event will be also considered as potential manuscripts for the Special Issue.

Prof. Ivano Benedetti
Prof. Fabrice Barbe
Prof. Antonio Caggiano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Micro-structure-based modelling
  • Micro- and multi-scale modelling
  • Full-field modelling
  • Complex microstructures
  • Multi-physics simulations
  • Computational homogenization
  • Composite and heterogeneous materials
  • Polycrystalline materials
  • Lattices and honeycombs
  • Cohesive-frictional materials
  • Architectured materials
  • Multi-physics and multi-functional materials
  • Lightweight materials for aerospace applications
  • Biological and bio-inspired materials
  • Micro-damage and micro-cracking modelling
  • Microstructure evolution simulation
  • Crystal plasticity
  • Strain-rate aspects
  • Time-dependent behaviours
  • Fatigue and cycling loads.

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Published Papers (3 papers)

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Research

21 pages, 5604 KiB  
Article
Predicting the Non-Deterministic Response of a Micro-Scale Mechanical Model Using Generative Adversarial Networks
by Albert Argilaga and Duanyang Zhuang
Materials 2022, 15(3), 965; https://doi.org/10.3390/ma15030965 - 26 Jan 2022
Cited by 3 | Viewed by 3200
Abstract
Recent improvements in micro-scale material descriptions allow to build increasingly refined multiscale models in geomechanics. This often comes at the expense of computational cost which can eventually become prohibitive. Among other characteristics, the non-determinism of a micro-scale response makes its replacement by a [...] Read more.
Recent improvements in micro-scale material descriptions allow to build increasingly refined multiscale models in geomechanics. This often comes at the expense of computational cost which can eventually become prohibitive. Among other characteristics, the non-determinism of a micro-scale response makes its replacement by a surrogate particularly challenging. Machine Learning (ML) is a promising technique to substitute physics-based models, nevertheless existing ML algorithms for the prediction of material response do not integrate non-determinism in the learning process. Is it possible to use the numerical output of the latest micro-scale descriptions to train a ML algorithm that will then provide a response at a much lower computational cost? A series of ML algorithms with different levels of depth and supervision are trained using a data-driven approach. Gaussian Process Regression (GPR), Self-Organizing Maps (SOM) and Generative Adversarial Networks (GANs) are tested and the latter retained because of its superior results. A modified GANs with lower network depth showed good performance in the generation of failure probability maps, with good reproduction of the non-deterministic micro-scale response. The trained generator can be incorporated into existing multiscale models allowing to, at least partially, bypass the costly micro-scale computations. Full article
(This article belongs to the Special Issue Advances in Computational Materials Micro-Mechanics)
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28 pages, 6586 KiB  
Article
Buckling Sensitivity of Tow-Steered Plates Subjected to Multiscale Defects by High-Order Finite Elements and Polynomial Chaos Expansion
by Alberto Racionero Sanchez-Majano, Alfonso Pagani, Marco Petrolo and Chao Zhang
Materials 2021, 14(11), 2706; https://doi.org/10.3390/ma14112706 - 21 May 2021
Cited by 30 | Viewed by 2289
Abstract
It is well known that fabrication processes inevitably lead to defects in the manufactured components. However, thanks to the new capabilities of the manufacturing procedures that have emerged during the last decades, the number of imperfections has diminished while numerical models can describe [...] Read more.
It is well known that fabrication processes inevitably lead to defects in the manufactured components. However, thanks to the new capabilities of the manufacturing procedures that have emerged during the last decades, the number of imperfections has diminished while numerical models can describe the ground truth designs. Even so, a variety of defects has not been studied yet, let alone the coupling among them. This paper aims to characterise the buckling response of Variable Stiffness Composite (VSC) plates subjected to spatially varying fibre volume content as well as fibre misalignments, yielding a multiscale sensitivity analysis. On the one hand, VSCs have been modelled by means of the Carrera Unified Formulation (CUF) and a layer-wise (LW) approach, with which independent stochastic fields can be assigned to each composite layer. On the other hand, microscale analysis has been performed by employing CUF-based Mechanics of Structure Genome (MSG), which was used to build surrogate models that relate the fibre volume fraction and the material elastic properties. Then, stochastic buckling analyses were carried out following a multiscale Monte Carlo analysis to characterise the buckling load distributions statistically. Eventually, it was demonstrated that this multiscale sensitivity approach can be accelerated by an adequate usage of sampling techniques and surrogate models such as Polynomial Chaos Expansion (PCE). Finally, it has been shown that sensitivity is greatly affected by nominal fibre orientation and the multiscale uncertainty features. Full article
(This article belongs to the Special Issue Advances in Computational Materials Micro-Mechanics)
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20 pages, 2181 KiB  
Article
The Meshless Analysis of Scale-Dependent Problems for Coupled Fields
by Jan Sladek, Vladimir Sladek and Pihua H. Wen
Materials 2020, 13(11), 2527; https://doi.org/10.3390/ma13112527 - 2 Jun 2020
Cited by 4 | Viewed by 1873
Abstract
The meshless local Petrov–Galerkin (MLPG) method was developed to analyze 2D problems for flexoelectricity and higher-grade thermoelectricity. Both problems were multiphysical and scale-dependent. The size effect was considered by the strain and electric field gradients in the flexoelectricity, and higher-grade heat flux in [...] Read more.
The meshless local Petrov–Galerkin (MLPG) method was developed to analyze 2D problems for flexoelectricity and higher-grade thermoelectricity. Both problems were multiphysical and scale-dependent. The size effect was considered by the strain and electric field gradients in the flexoelectricity, and higher-grade heat flux in the thermoelectricity. The variational principle was applied to derive the governing equations within the higher-grade theory of considered continuous media. The order of derivatives in the governing equations was higher than in their counterparts in classical theory. In the numerical treatment, the coupled governing partial differential equations (PDE) were satisfied in a local weak-form on small fictitious subdomains with a simple test function. Physical fields were approximated by the moving least-squares (MLS) scheme. Applying the spatial approximations in local integral equations and to boundary conditions, a system of algebraic equations was obtained for the nodal unknowns. Full article
(This article belongs to the Special Issue Advances in Computational Materials Micro-Mechanics)
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