• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.315-321
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• 2012

In this study, a sequential multiscale homogenization method to characterize the effective thermal conductivity of nano particulate polymer nanocomposites is proposed through a molecular dynamics(MD) simulations and a finite element-based homogenization method. The thermal conductivity of the nanocomposites embedding different-sized nanoparticles at a fixed volume fraction of 5.8% are obtained from MD simulations. Due to the Kapitza thermal resistance, the thermal conductivity of the nanocomposites decreases as the size of the embedded nanoparticle decreases. In order to describe the nanoparticle size effect using the homogenization method with accuracy, the Kapitza interface in which the temperature discontinuity condition appears and the effective interphase zone formed by highly densified matrix polymer are modeled as independent phases that constitutes the nanocomposites microstructure, thus, the overall nanocomposites domain is modeled as a four-phase structure consists of the nanoparticle, Kapitza interface, effective interphase, and polymer matrix. The thermal conductivity of the effective interphase is inversely predicted from the thermal conductivity of the nanocomposites through the multiscale homogenization method, then, exponentially fitted to a function of the particle radius. Using the multiscale homogenization method, the thermal conductivities of the nanocomposites at various particle radii and volume fractions are obtained, and parametric studies are conducted to examine the effect of the effective interphase on the overall thermal conductivity of the nanocomposites.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.8 s.227
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• pp.968-975
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• 2004

Nanofluids have anomalously high thermal conductivities at very low fraction, strongly temperature-dependent and size-dependent conductivities, and three-fold higher critical heat flux than that of base fluids. Traditional conductivity theories such as the Maxwell or other macroscale approaches cannot explain why nanofluids have these intriguing features. So in this paper, we devise a theoretical model that accounts for the fundamental role of dynamic nanoparticles in nanofluids. The proposed model not only captures the concentration and temperature-dependent conductivity, but also predicts strongly size-dependent conductivity. Furthermore, we physically explain the new phenomena for nanofluids. In addition, based on a proposed model, the effects of various parameters such as the ratio of thermal conductivity of nanofluids to that of a base fluid, volume fraction, nanoparticle size, and temperature on the thermal conductivities of nanofluids are investigated.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1388-1393
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• 2004

Investigators have been perplexed with the thermal phenomena behind the recently discovered nanofluids, fluids with unprecedented stability of suspended nanoparticles although huge difference in the density of nanoparticles and fluid. For example, nanofluids have anomalously high thermal conductivities at very low fraction, strongly temperature-dependent and size-dependent conductivities, and three-fold higher critical heat flux than that of base fluids. Traditional conductivity theories such as the Maxwell or other macroscale approaches cannot explain why nanofluids have these intriguing features. So in this paper, we devise a theoretical model that accounts for the fundamental role of dynamic nanoparticles in nanofluids. The proposed model not only captures the concentration and temperature-dependent conductivity, but also predicts strongly size-dependent conductivity. Furthermore, we physically explain the new phenomena for nanofluids. In addition, based on a proposed model, the effects of various parameters such as the ratio of thermal conductivity of nanofluids to that of a base fluid, volume fraction, nanoparticle size, and temperature on the thermal conductivities of nanofluids are investigated.