• 한국전산구조공학회논문집
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• 제25권4호
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• pp.315-321
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• 2012

본 연구에서는 분자동역학 전산모사와 유한요소해석 기반의 균질화 기법을 통해 나노복합재의 열전도 특성을 정확하고 효율적으로 예측할 수 있는 순차적 멀티스케일 균질화 해석기법을 제안하였다. 나노입자의 크기효과가 나노복합재의 유효 열전도 특성에 미치는 영향을 조사하기 위해 크기가 다른 구형 나노입자가 첨가된 나노복합재의 열전도 계수를 분자동역학 전산모사를 통해 예측했고, 그 결과 나노입자의 크기가 작아질수록 계면에서의 Kapitza열저항에 의해 나노복합재의 열전도 계수가 점차 감소하는 것으로 나타났다. 이러한 나노입자의 크기효과를 균질화 해석모델을 통해 정확하게 묘사하기 위해 Kapitza 열저항에 의한 계면에서의 온도 불연속 구간과 고분자 기지가 높은 밀도를 가지며 흡착되는 유효계면을 추가적인 상으로 도입하여 나노복합재를 입자, Kapitza 계면, 유효계면, 기지로 구성된 4상의 연속체 구조로 모델링하였다. 이후 순차적 멀티스케일 균질화 해석기법을 통해 유효계면의 열전도 계수를 나노복합재의 열전도 계수로부터 역으로 예측했으며, 이를 입자의 반경에 대한 함수로 근사하였다. 근사 함수를 토대로 다양한 입자 체적분율과 반경에 대한 나노복합재의 유효 열전도 특성을 예측하였으며, 유효계면에 대한 매개변수 연구를 수행하였다.

• Journal of information and communication convergence engineering
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• 제20권4호
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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.

• 대한기계학회논문집B
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• 제28권8호
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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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.