Estimating thermal conductivities and elastic moduli of porous ceramics using a new microstructural parameter

Guan, Kang; Ren, Haitao; Zeng, Qingfeng; Feng, Zhiqiang; Wu, Jian; Lu, Zhenya; Rao, Pinggen; Cheng, Yangfang; Gong, Zhenyuan; Yu, Yingfeng

doi:10.1016/j.jeurceramsoc.2018.09.011

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…Besides, MD simulation is computationally expensive and time-consuming, which limits its applications as screening tools for specific materials. Lately, machine learning methods have been applied to predict composite thermal conductivity, ITR between graphene and boron nitride, and thermoelectric conversion efficiency [30][31][32][33][34][35][36][37][38] . Specifically, Xu group 8 applied machine learning algorithms as regression tree ensembles of LSBoost, support vector machines, and Gaussian regress processes to build ITR prediction models.…”

Section: Xiaojuan Tian 1* and Mingguang Chen 2*mentioning

confidence: 99%

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Tian

Chen

2021

Sci Rep

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Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X = 20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.

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Section: Xiaojuan Tian 1* and Mingguang Chen 2*mentioning

confidence: 99%

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Tian

Chen

2021

Sci Rep

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“…The generation of porous model is completed by the following scheme. The previous general process of forming the porous material numerically is to randomly choose unchanged core points and consider surrounding materials with a very small elasticity to achieve different porosities [30,34]. If any position in the 3D space inside the model boundary can be selected as the pore's core, the element may not be deleted when the pore radius or the number of elements is small (large volume element).…”

Section: Generation Of Porous Microstructuresmentioning

confidence: 99%

“…This is where the scheme branches off, forming different ways to process different functional requirements, thus implementing the different distribution modes of pores. Based on the previous study, the shape and the distribution of the pores are the two main factors that influence the mechanical properties of porous material [12,30,37,39]. Therefore, we will show how the reconstruction of the porous model is done numerically in different scenarios.…”

Section: Generation Of Porous Microstructuresmentioning

confidence: 99%

“…The implementation of the IS algorithm is a little more complicated. There are already some existing strategies to this problem in the previous literature [30]: by extending the distance between two adjacent cores, the two pores could be isolated until the distance between two cores is larger than the sum of the two pore radii, which means:…”

Section: Numerical Simulation Of the Pores' Distributionmentioning

confidence: 99%

“…Hereafter, the relative arrangement of pores, i.e., distribution mode, among different randomly-distributed pores, is taken into account in modeling the porous microstructures in Ref. [28][29][30]. Recently, the influence of some more complex and irregular shapes of pores are considered in generating the porous models [15,16,18,[31][32][33][34][35], and some particular schemes, say hole forming agent method and space-holder technique, are developed simultaneously to reconstruct the porous model more efficiently.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite Element Modeling of Porous Microstructures With Random Holes of Different-Shapes and -Sizes to Predict Their Effective Elastic Behavior

Dong

Liu

et al. 2019

Applied Sciences

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Porous materials are promising media for designing medical instruments, drug carriers, and bioimplants because of their excellent biocompatibility, ease of design, and large variation of elastic moduli. In this study, a computational strategy using the finite element method is developed to model the porous microstructures and to predict the relevant elastic moduli considering the actual characteristics of the micropores and their distributions. First, an element-based approach is presented to generate pores of different shapes and sizes according to the experimental observations. Then, a computational scheme to evaluate the effective moduli of macroscopically isotropic porous materials based on their micro-mechanics is introduced. Next, the accuracy of our approach is verified with the analytical solutions of the extreme bounds of the elastic isotropic moduli of a simplified model and with the experimental data available in the literature. Finally, the influence of the shape of pores and their distribution modes are assessed.

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Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

Guan

Gao

Zeng

et al. 2020

Chinese Journal of Chemical Engineering

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Estimating thermal conductivities and elastic moduli of porous ceramics using a new microstructural parameter

Cited by 4 publications

References 22 publications

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Descriptor selection for predicting interfacial thermal resistance by machine learning methods

Finite Element Modeling of Porous Microstructures With Random Holes of Different-Shapes and -Sizes to Predict Their Effective Elastic Behavior

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

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