Effects of particles size on the overall strength of nanocomposites: Molecular Dynamics simulations and theoretical modeling

Lucchetta, Antoine; Brach, Stella; Kondo, Djimédo

doi:10.1016/j.mechrescom.2021.103669

Cited by 9 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Lucchetta et al [ 53 ] assessed the strength properties of particulate-reinforced nanocomposite using molecular dynamics analysis of a cubic cell comprised of an aluminium matrix and a spherical nickel nanoparticle. The authors found that the effective strength increases with the inclusion size reduction at a constant reinforcement volume fraction.…”

Section: Void Nucleationmentioning

confidence: 99%

Voids Development in Metals: Numerical Modelling

Wciślik

Lipiec

2023

Materials

View full text Add to dashboard Cite

The article is a continuation of two previous review papers on the fracture mechanism of structural metals through the nucleation, growth and coalescence of voids. In the present paper, the literature on the numerical modelling of void nucleation and development has been reviewed. The scope of the work does not include porous material models and their numerical implementation. As part of the discussion on void initiation, nucleation around second phase particles and nucleation as an effect of the discontinuity of the crystal structure were discussed separately. The basic void cell models, finite element method (FEM) models of periodically distributed particles/voids and models based on the results of the observations of the actual microstructure of materials have been characterised. Basic issues related to the application of the cohesive approach in void nucleation modelling have been considered. A separate issue is the characteristics of atomistic simulations and peridynamic modelling, which have been developed in recent years. Numerical approaches to modelling the growth and coalescence of voids are described, with particular emphasis on the influence of the stress state and strain localisation. Basic conclusions from the simulation are presented, pointing to the contribution of FEM modelling to the understanding of microstructural phenomena leading to ductile fracture.

show abstract

Section: Void Nucleationmentioning

confidence: 99%

Voids Development in Metals: Numerical Modelling

Wciślik

Lipiec

2023

Materials

View full text Add to dashboard Cite

show abstract

“…In [21] the interphase effects in graphene-polymer nanocomposites are studied using molecular dynamics, and a corresponding continuum model based on imperfect interfaces is developed. Lucchetta et al identify the interfacial strength to be filler-size-dependent based on MD simulations [22]. Furthermore, numerical studies allow gaining a deeper insight into new materials without expensive experiments.…”

Section: Introduction and Previous Work 1interphases And Size Effects...mentioning

confidence: 99%

A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects

Ries

Weber

Possart

et al. 2022

Composites Part A: Applied Science and Manufacturing

View full text Add to dashboard Cite

“…A size-dependency of the elastic properties is particularly expected when it comes to nanoporous materials which are attributed to the competition between surface and bulk energies, especially at smaller scales [6][7][8]. Classical models of elasticity should be extended correspondingly through the incorporation of surface elasticity models [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence

Mianroodi¹,

Rezaei²,

Siboni³

et al. 2021

Preprint

View full text Add to dashboard Cite

The elastic properties of materials derive from their electronic and atomic nature.However, simulating bulk materials fully at these scales is not feasible, so that typically homogenized continuum descriptions are used instead. A seamless and lossless transition of the constitutive description of the elastic response of materials between these two scales has been so far elusive. Here we show how this problem can be overcome by using Artificial Intelligence (AI). A Convolutional Neural Network (CNN) model is trained, by taking the structure image of a nanoporous material as input and the corresponding elasticity tensor, calculated from Molecular Statics (MS), as output. Trained with the atomistic data, the CNN model captures the size-and poredependency of the material's elastic properties which, on the physics side, can stem from surfaces and non-local effects. Such effects are often ignored in upscaling from atomistic to classical continuum theory. To demonstrate the accuracy and the efficiency of the trained CNN model, a Finite Element Method (FEM) based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that by a full atomistic simulation. The good agreement between the atomistic simulations and the FEM-AI combination for a system with size and surface effects establishes a new lossless scale bridging approach to such problems. The trained CNN model deviates from the atomistic result by 9.6% for porosity scenarios of up to 90% but it is about 230 times faster than the MS calculation and does not require to change simulation methods between different scales. The efficiency of the CNN evaluation together with the preservation of important atomistic effects makes the trained model an effective atomistically-informed constitutive model for macroscopic simulations of nanoporous materials, optimization of nano-structures, and solving of inverse problems.

show abstract

Effects of particles size on the overall strength of nanocomposites: Molecular Dynamics simulations and theoretical modeling

Cited by 9 publications

References 37 publications

Voids Development in Metals: Numerical Modelling

Voids Development in Metals: Numerical Modelling

A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects

Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence

Contact Info

Product

Resources

About