کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8942048 1645051 2018 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods
ترجمه فارسی عنوان
پیش بینی هدایت حرارتی موثر مواد کامپوزیتی و رسانه متخلخل با روش های یادگیری ماشین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی جریان سیال و فرایندهای انتقال
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Heat and Mass Transfer - Volume 127, Part C, December 2018, Pages 908-916
نویسندگان
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