Stochastic finite element analysis framework for modelling thermal conductivity of particulate modified polymer composites

Moghaddam, Hamidreza Ahmadi

doi:10.1016/j.rinp.2018.10.045

Cited by 12 publications

(21 citation statements)

References 38 publications

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“…The nature of stochastic analysis, and thus the presented modeling approach, requires performing numerous iterations in order to calculate the effective thermal conductivity of a polymer matrix containing a rod-shaped filler. The SFEA algorithm described in [27] was adopted and employed for the present study. This algorithm provides a framework for connecting a customized stochastic analysis with a parametric finite element analysis (FEA).…”

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

“…The modeling framework, which uses several scripting languages, is briefly summarized in the present section. The interested reader is referred to [27] for additional details on the modeling approach. Figure 1 depicts a flowchart outlining the main domain, i.e., the elemental structure and connections, of the framework's various modules.…”

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

“…The RNG module sequentially creates sets of random numbers for anchor points in Cartesian coordinates as well as vectors required for generating the Figure 2. Monte Carlo simulation (MCS) module algorithm [27].…”

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

“…It should be mentioned that the RNG module also has the ability of creating particles that conform to a given size distribution (whilst, this feature was not utilized in the present study). The interested reader is referred to [27] for details on the algorithm that produces particles obeying a certain size distribution, and the effect that different size-ordered particle addition has on computational performance.…”

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

“…Stochastic analysis can be used in a variety of applications, e.g., financial forecasting and modeling, where numerous input and output variables need to be considered. Recently, a numerical modeling framework was developed based stochastic analysis to simulate the effective material properties of filler modified composites [27]. Specifically, a stochastic finite element analysis (SFEA) approach was employed that enabled the prediction of the effective thermal conductivity of randomly distributed and disperses spherical particles embedded in a polymer matrix.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Finite Element Modelling of Char Forming Filler Addition and Alignment – Effects on Heat Conduction into Polymer Condensed Phase

Moghaddam¹,

Mertiny²

2019

Flame Retardants

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Micro-and nano-filler particles have been considered as char-forming flame retardants for polymers. It has been shown that suitable particles may operate in the condensed phase to prevent or delay the escape of fuel into the gas phase. Good flame retardancy performance may be achieved in composites with comparatively low filler loadings. However, many candidate filler materials, such as rod-like and plate-like carbon allotrope fillers with high aspect ratio, will effectively enhance the composite's thermal conductivity, and hence, may greatly increase heat input into the condensed phase. Moreover, anisotropy in terms of thermal conductivity must be considered when rod-like and plate-like particles are aligned, for example as a result of manufacturing processes. The presented study investigates these effects, i.e., thermal conductivity enhancement due to filler addition and alignment, using a modeling framework based on Monte Carlo simulation that was developed for predicting effective composite properties considering filler-matrix and particle-to-particle interfacial effects. A stochastic finite element analysis was performed to model rod-shaped carbon particles embedded in a polymer matrix. The chosen analysis is demonstrated to be an effective means for elucidating the effect of filler addition and alignment on the heat conduction into polymer materials containing fillers as charforming flame retardants.

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Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

Section: Stochastic Finite Element Analysis Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stochastic Finite Element Modelling of Char Forming Filler Addition and Alignment – Effects on Heat Conduction into Polymer Condensed Phase

Moghaddam¹,

Mertiny²

2019

Flame Retardants

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Numerical simulation and experimental study on mesomechanics properties of particulate composites

Liu,

Qian,

Dai

et al. 2024

Polymer Composites

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At present, it exists the problems of poor computational accuracy of the micromechanical model and insufficient adaptive matching of the RVE unit cell model boundary conditions, being caused by the random distribution of particle size and quantity in particle composite materials. As for the above problems, the paper proposes a micromechanical model that uses a random sequence adsorption algorithm to deeply match and enhance the random distribution of particles, and a subroutine joint simulation method to generate boundary conditions quickly and automatically. The RVE unit cell model of particulate composites are numerically simulated at the micromechanical level. It was found that the enhancement of the mechanical properties of the base material by enhancing particles is highly consistent with the actual improvement value. The simulation values and actual values of particle composite materials with different volume contents are also accurately consistent. In addition, with the increase of particle volume content, its reinforcement effect on the base material first increases and then decreases, and there exists an optimal mixing ratio and a minimum mixing ratio. The optimal mixing ratio for short cut fiber reinforced particles is 50%, with a minimum mixing ratio of 10%. The optimal mixing ratio for silicon carbide reinforced particles is 40%, with a minimum mixing ratio of 8%. Through experimental results, it has been verified that the micromechanical model can quickly and accurately predict the elastic constants and Poisson's ratio of composite materials under periodic displacement boundary conditions, with a prediction accuracy of within 10%.Highlights Effectively solve the problems such as the random distribution of the size and quantity of reinforced particles in the particle composite material and the difficulty in adding the boundary conditions of the micro‐RVE unit cell model in the macro composite material. A random sequence adsorption algorithm is proposed to generate the RVE cell model with reinforced particles, and the periodic displacement boundary conditions are added to the RVE cell model through Python and Abaqus joint simulation. The simulation results show that the stress of chopped fiber reinforced particles is about 1.96 times that of the matrix material, and the stress of SiC reinforced particles is about 2.1 times that of the matrix material, and the optimal mixing ratio of chopped fiber reinforced particles is 50% and that of silicon carbide reinforced particles is 40%. The proposed micromechanical model can accurately predict the elastic constants of composites under periodic boundary conditions, and its prediction accuracy can be controlled within 10%. In this paper, a more reasonable meso‐stress‐strain field of single cell element is given, which can provide reference for studying the meso‐failure mechanism and nonlinear mechanical behavior of particle composites.

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Thermal performance and heat transfer mechanism of EGa-In-Sn/W composite thermal interface materials

Wang,

Wei,

et al. 2024

J Mater Sci: Mater Electron

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Stochastic finite element analysis framework for modelling thermal conductivity of particulate modified polymer composites

Cited by 12 publications

References 38 publications

Stochastic Finite Element Modelling of Char Forming Filler Addition and Alignment – Effects on Heat Conduction into Polymer Condensed Phase

Stochastic Finite Element Modelling of Char Forming Filler Addition and Alignment – Effects on Heat Conduction into Polymer Condensed Phase

Numerical simulation and experimental study on mesomechanics properties of particulate composites

Thermal performance and heat transfer mechanism of EGa-In-Sn/W composite thermal interface materials

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