Modeling of the effective thermal conductivity of composite materials with FEM based on resistor networks approach

Yu, Chan; Zhang, Yan; Hu, Zhili; Liu, Johan; Cheng, Zhaonian

doi:10.1007/s00542-009-0984-1

Cited by 26 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was found that the thermal conductivity of polymer-based composites can be efficiently improved by introduction of conductive fillers, and can be influenced by the shape and content of the fillers in matrix [9][10][11][12]. At the same time, various semi-empirical or empirical models have been developed to qualitatively simulate the thermal conductivity of dual-phase polymer matrix composites [13][14][15][16][17][18][19][20][21][22]. There are only few models developed for three-phase cases by Agari et al [17] and Zeng et al [23].…”

Section: Introductionmentioning

confidence: 99%

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Wang

Cai

Pei

et al. 2011

Sci. China Phys. Mech. Astron.

View full text Add to dashboard Cite

Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Wang

Cai

Pei

et al. 2011

Sci. China Phys. Mech. Astron.

View full text Add to dashboard Cite

show abstract

“…To this end, we use a numerical model that is based on RRNs to simulate the thermoelectric properties of composites. RRNs can be used as an alternative to state‐of‐the‐art EMTs [ 22,24–29 ] and have proven to be powerful for describing effects from charge transport and heat transport in composite media to fluid flow in porous materials. By discretizing space into finite elements (sites), we model the material as a large linear network.…”

Section: Introductionmentioning

confidence: 99%

Improved Electrical, Thermal, and Thermoelectric Properties Through Sample‐to‐Sample Fluctuations in Near‐Percolation Threshold Composite Materials

Rösch

Giunta

Mallick

et al. 2021

Advcd Theory and Sims

View full text Add to dashboard Cite

Effective medium theories (EMT) are powerful tools to calculate sample averaged thermoelectric material properties of composite materials. However, averaging over the heterogeneous spatial distribution of the phases can lead to incorrect estimates of the thermoelectric transport properties and the figure of merit ZT in compositions close to the percolation threshold. This is particularly true when the phases’ electronic properties are rather distinct leading to pronounced percolation effects. The authors propose an alternative model to calculate the thermoelectric properties of multi‐phased materials that are based on an expanded nodal analysis of random resistor networks (RRN). This method conserves the information about the morphology of the individual phases, allowing the study of the current paths through the phases and the influence of heterogeneous charge transport and cluster formation on the effective material properties of the composite. The authors show that in composites with strongly differing phases close to the percolation threshold the thermoelectric properties and the ZT value are always dominated exclusively by one phase or the other and never by an average of both. For these compositions, the individual samples display properties vastly different from EMT predictions and can be exploited for an increased thermoelectric performance.

show abstract

“…10,16–18 However, PMC’s k eff estimated by these models do not have satisfactory agreement with experimental data when the filler loadings are high. 18,19…”

Section: Introductionmentioning

confidence: 99%

“…10,[16][17][18] However, PMC's k eff estimated by these models do not have satisfactory agreement with experimental data when the filler loadings are high. 18,19 While various experimental studies have been conducted to investigate the effect of fillers on foam morphology 20 and the improvement of electrical and/or mechanical properties by foaming polymer composite and nanocomposite, [21][22][23] studies that investigated the enhancement of PMC's k eff by foaming were scarce. Recently, the fabrication of light-weight thermally conductive linear low density polyethylene (LLDPE)-hexagonal boron nitride (hBN) composite foams with k eff slightly over 3.0 W m -1 K -1 was reported, and the increase in PMC foam's k eff was believed to be attributed to the enhanced alignment of hBN micro-platelets in the LLDPE matrix despite the presence of thermally insulating air voids.…”

Section: Introductionmentioning

confidence: 99%

Modelling of effective thermal conductivity of polymer matrix composite foams with biaxially aligned filler networks

Ding

Leung

2015

Journal of Cellular Plastics

View full text Add to dashboard Cite

Recent research revealed potentials to develop polymer matrix composite foams filled with thermally conductive filler network as light-weight thermal management materials. Since polymeric foams are commonly used for thermal insulation, the concept of thermally conductive polymer matrix composite foams seems to be counter-intuitive, and the underlying factors that govern polymer matrix composite foam's effective thermal conductivity (k eff ) were not clear. In this context, this paper develops new models to predict polymer matrix composite foams' k eff and to elucidate the dependence of k eff on their cellular morphology. Linear low density polyethylene-hexagonal boron nitride composite foams were used as case examples to verify the model. The model demonstrated that the composite foam's k eff would be promoted when the volume expansion was over a threshold percentage. At low hexagonal boron nitride loadings (e.g. 10 vol.%) and fixed cell size, linear low density polyethylene-hexagonal boron nitride foams' k eff increased with volume expansion percent through an increase in cell population density. Constrained foaming with preferential expansion in the heat flow direction also enhanced their k eff .

show abstract

Modeling of the effective thermal conductivity of composite materials with FEM based on resistor networks approach

Cited by 26 publications

References 21 publications

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Improved Electrical, Thermal, and Thermoelectric Properties Through Sample‐to‐Sample Fluctuations in Near‐Percolation Threshold Composite Materials

Modelling of effective thermal conductivity of polymer matrix composite foams with biaxially aligned filler networks

Contact Info

Product

Resources

About