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

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" (Fathidoost, 2024). 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 (Fathidoost, 2024). White and black colors within the images represent distinct regions of the material system, respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work. Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis Param. Mean [unit] Std. \(D_{1}\) 40, 50, 60 [μm] 20% \(D_{2}\) 20, 25, 26, 30, 33, 40 [μm] 30% \(V_\mathrm{f}\) 1.5, 2.0 - \(A_\mathrm{r}\) 35, 40, 45, 55, 60 [%] - This dataset contains: dataset.csv: 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. microstructures_images.zip: containing binary cross-section images of the RVEs from synthetic microstructures。 results.zip: contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.