Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods
By Han Wei, Shuaishuai Zhao, Qingyuan Rong, Hua Bao. International Journal of Heat and Mass Transfer, 2018
297 citations recorded by the source corpus.
Abstract Composite materials have a wide range of engineering applications, and their effective thermal conductivities are important thermo-physical properties for real applications. The traditional methods to study effective thermal conductivities of composite materials, such as the effective medium theory, the direct solution of heat diffusion equation, or the Boltzmann transport equation, are all based on developing good physical understanding of heat transfer mechanisms in those composite materials. In this work, we take a completely different approach to predict the effective thermal conductivities of composite materials using machine learning methods. With a set of trustable data, the support vector regression (SVR), Gaussian process regression (GPR) and convolution neural network (CNN) are employed to train models that can predict the effective thermal conductivities of composite materials. We find that the models obtained from SVR, GPR, and CNN all have a better performance than the Maxwell-Eucken model and the Bruggeman model in terms of predicting accuracy. Our work demonstrates that machine learning methods are useful tools to fast predict the effective thermal conductivities of composite materials and porous media if the training data set is available. The machine learning approach also has the potential to be generalized and applied to study other physical properties of composite materials.
Subfields: Materials Chemistry, Mechanical Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/8d4a71a22e6cef5132b9a5dd55713403c9580e89
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Citations in the corpus
- Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “ML models have been shown to outperform physics-based models in many disciplines (e.g., materials science [148, 238, 285], applied physics [22, 127], aquatic sciences [132, 286], atmospheric science [206], biomedical science [264], computational biology [9]).”
- Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “ML models have been shown to outperform physics-based models in many disciplines (e.g., materials science [130, 220, 263], applied physics [15, 116], aquatic sciences [120], atmospheric science [185], biomedical science [245], computational biology [3]).”