A dataset for conduction heat transfer and deep learning

Published: 27 April 2022| Version 2 | DOI: 10.17632/rw9yk3c559.2


Big data images for conduction heat transfer The related paper has been published here: Edalatifar, Mohammad, Mohammad Bagher Tavakoli, Mohammad Ghalambaz, and Farbod Setoudeh. "Using deep learning to learn physics of conduction heat transfer." Journal of Thermal Analysis and Calorimetry 146, no. 3 (2021): 1435-1452. Links: https://doi.org/10.1007/s10973-020-09875-6 https://link.springer.com/article/10.1007/s10973-020-09875-6 Mohammad Edalatifar, Mohammad Ghalambaz, Mohammad Bagher Tavakoli, Farbod Setoudeh, New loss functions to improve deep learning estimation of heat transfer, Neural Computing and Applications Link: https://doi.org/10.1007/s00521-022-07233-1 Keywords: Deep convolutional neural networks - Loss function - Heat transfer images - Physical images - Artificial intelligence - CFD - Computational fluids dynamics - Finite volume method - Laplace equation - Dirichlet boundary condition


Steps to reproduce

The dataset is saved in two format, .npz for python and .mat for matlab. *.mat has large size, then it is compressed with winzip. ReadDataset_Python.py and ReadDataset_Matlab.m are examples of read data using python and matlab respectively. For use dataset in matlab download Dataset/HeatTransferPhenomena_35_58.zip, unzip it and then use ReadDataset_Matlab.m as an example. In case of python, download Dataset/HeatTransferPhenomena_35_58.npz and run ReadDataset_Python.py.


Laplace Transformation, Conductive Heat Transfer, Convolutional Neural Network, Deep Learning