Abstract
Deep learning’s role in tackling complicated engineering problems becomes more and more effective by advances in computer science. One of the classical problems in physics is representing the solution of heat propagation in the arbitrary 2-D domain. Study of two-dimensional heat transfer provides a precious bed for related physical issues. In this work, by using finite volume method, we solved the two-dimensional heat equation on the arbitrary domain with specified limitations (considering three heated rectangular obstacles inside the main domain) for 100000 different cases. These cases were divided into big batches in order to reduce the computational cost. The solution for each case was used as sample data to train our deep neural network. After the training process, deep learning results have been compared to results which were produced by the commercial program (ANSYS). After analyzing deep learning efficiency, obviously, our network successfully was able to predict the solution of heat transfer physics with satisfactory precision.
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Zakeri, B., Monsefi, A.K., Darafarin, B. (2020). Deep Learning Prediction of Heat Propagation on 2-D Domain via Numerical Solution. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_13
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