2D Binary Images and Effective Thermal Conductivity CFD Results

Description

This dataset was originally created to train a CNN for predicting the effective thermal conductivity of these binary structures based on geometry alone. A total of four different ratios of thermal conductivity between the two phases were simulated. The original dataset contains 40,000 unique 128x128 binary structures, and is further expanded by flipping the color scheme, rotating the image 90 degrees, and doing both simultaneously. That is done to the folders CirclePack, EllipsePack, and QuadrilateralPack, expanding the dataset to 130,000 unique structures. Therefore, in the folders below are the 40,000 original images (10,000 in each folder) and all of the CFD results (520,000 total simulation results). For more detail on structure generation and the CFD algorithm, refer to the manuscript. Pre-proof manuscript: Adam, A., Fang, H., & Li, X. (2023). Effective thermal conductivity estimation using a convolutional neural network and its application in topology optimization. In Energy and AI (p. 100310). Elsevier BV. https://doi.org/10.1016/j.egyai.2023.100310

Steps to reproduce

Code used for the creation of the binary structures can be made available through any of the authors. The CFD code used to calculate the effective thermal conductivity can be found on GitHub here: https://github.com/adama-wzr/Keff-CFD The GitHub repository is a work in progress and is constantly being updated. Please direct any questions to one of the contributors via email.

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