Advanced Thermal Management Materials

Jiang, Guosheng; Diao, Liyong; Kuang, Ken

doi:10.1007/978-1-4614-1963-1

Cited by 26 publications

(6 citation statements)

References 60 publications

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“…In order to address this, there are several approaches including cryogenic coolers, fan-blown air cooling, heat sinks, and micropumps. [246,247] In this context, one effective technique is the application of thermal management materials such as high thermally conductive polymeric composites to provide efficient heat dissipation routes in microelectronic packaging. Considering their lightweight, processability, and low cost, they are promising materials for microelectronics packaging.…”

Section: Discussionmentioning

confidence: 99%

A review on high thermally conductive polymeric composites

Ghaffari‐Mosanenzadeh

Tafreshi

Dammen‐Brower

et al. 2021

Polymer Composites

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Polymers are known as thermally insulated materials with reported effective thermal conductivity (K eff ) in the range of 0.1 to 0.5 Wm À1 K À1 . However, increasing demand for smaller and more powerful electronics has created the need for thermally conductive polymers for use in heat exchangers and electronic packaging applications. Given this background, much research has been done over the past two decades to increase the K eff of polymers. Based on the strategy involved, those works can be divided into two main categories:(i) increasing the K eff of the neat polymer by aligning its chains orientation; and (ii) increasing polymer K eff by fabrication of polymeric composites with thermally conductive filler networks. Among these two strategies, the former is limited to nanoscale laboratory research and is difficult to scale up for mass production. Therefore, this work is mainly focused on the latter category, thermally conductive polymeric composites, which has a higher potential for large-scale production. This work aims to summarize, evaluate, and highlight the successful strategies of the recent efforts in enhancing the thermal conductivity of polymer composites. The major achievements, future challenges, and the outlooks of high thermally conductive polymeric composites are presented by analyzing the results of about 300 works.

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Section: Discussionmentioning

confidence: 99%

A review on high thermally conductive polymeric composites

Ghaffari‐Mosanenzadeh

Tafreshi

Dammen‐Brower

et al. 2021

Polymer Composites

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“…Here again, the approach of a lumped analytic model is taken, with certain parameters determined through numerical methods, measurements and literature [9], [10]. This allows determining the temperatures at selected points, such as the coolant temperature at the outer perimeter of the machine or the temperature in the center point of the magnets.…”

Section: B Thermal Modelmentioning

confidence: 99%

Influence of machine integration on the thermal behavior of a PM drive for hybrid electric traction

Paar

Kolbe

Muetze

2014

2014 IEEE Energy Conversion Congress and Exposition (ECCE)

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We present a method for fast identification of the thermal behavior of vehicle integrated electric drives to propose a better understanding of electric machine design tailored to driving cycles. A simple, but satisfactorily accurate calculation method for the estimation of machine losses, which is an input for the analytic thermal network, in the early design stage is introduced. The analytic models, which are verified using measurement data, are used for parameter and case studies for consideration of the thermal environment with focus on the gearbox. An improvement of machine performance using an alternative cooling approach which cools down the end windings is shown in the final section of the paper.

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“…Thermal management of microelectronics is unprecedentedly crucial due to the rapid miniaturization in semiconductor technology, as it is essential to achieving high compactness along with high performance and reliability [1][2][3][4] . With the application of electronic devices with higher power consumption in smaller packages, micro-/nanoscale thermal analysis is necessary to understand and predict the Joule-heating effect for the design and improvement of thermal packaging.…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Neural Networks for Solving Time-Dependent Mode-Resolved Phonon Boltzmann Transport Equation

Luo

Zhou

2023

Preprint

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The phonon Boltzmann transport equation (BTE) is a powerful tool for modeling and understanding micro-/nanoscale thermal transport in solids, where Fourier's law can fail due to non-diffusive effect when the characteristic length/time is comparable to the phonon mean free path/relaxation time. However, numerically solving phonon BTE can be computationally costly due to its high dimensionality, especially when considering mode-resolved phonon properties and time dependency. In this work, we demonstrate the effectiveness of physics-informed neural networks (PINNs) in solving time-dependent mode-resolved phonon BTE. The PINNs are trained by minimizing the residual of the governing equations, and boundary/initial conditions to predict phonon energy distributions, without the need for any labeled training data. The results obtained using the PINN framework demonstrate excellent agreement with analytical and numerical solutions. Moreover, after offline training, the PINNs can be utilized for online evaluation of transient heat conduction, providing instantaneous results, such as temperature distribution. It is worth noting that the training can be carried out in a parametric setting, allowing the trained model to predict phonon transport in arbitrary values in the parameter space, such as the characteristic length. This efficient and accurate method makes it a promising tool for practical applications such as the thermal management design of microelectronics.

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Advanced Thermal Management Materials

Cited by 26 publications

References 60 publications

A review on high thermally conductive polymeric composites

A review on high thermally conductive polymeric composites

Influence of machine integration on the thermal behavior of a PM drive for hybrid electric traction

Physics-Informed Neural Networks for Solving Time-Dependent Mode-Resolved Phonon Boltzmann Transport Equation

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