Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning

<p>This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>).</p> <p>This dataset containes the microstructure images (identified by particle diameters size \(D_1\) and \(D_2\) volume fraction \(V_\mathrm{f}\) and aspect ratio \(A_\mathrm{r}\)) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic \(\mathrm{(Hf,Ta)C/SiC}\) ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system, &nbsp;respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work.</p> <p>Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis</p> <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>\(D_{1}\)</td> <td>40, 50, 60 [&mu;m]</td> <td>20%</td> </tr> <tr> <td>\(D_{2}\)</td> <td>20, 25, 26, 30, 33, 40 [&mu;m]</td> <td>30%</td> </tr> <tr> <td>\(V_\mathrm{f}\)</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>\(A_\mathrm{r}\)</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> <p>This dataset contains:</p> <ul> <li><em>dataset.csv: </em>containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li><em>microstructures_images.zip</em>: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li><em>results.zip:</em> contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>