Identifying key features in reactive flows: A tutorial on combining dimensionality reduction, unsupervised clustering, and feature correlation

Abstract

This study examines the capabilities of a data-driven workflow for automated key feature identification in reactive flows. The proposed approach aims at expediting the analysis of chemical engineering datasets by generating an automatic and explainable classification of regions showcasing distinct physics. The three main steps of this process, i.e., dimensionality reduction, unsupervised clustering, and feature correlation are discussed. A previously published framework based on these steps is used to compare against our proposed workflow, which employs different and more modern algorithms. The theoretical and practical differences between the previous and current algorithms are demonstrated in full. Overall, the key feature identification capability of the updated workflow is shown to be faster, more accurate, more robust, and closer to human intuition than previous methods. Throughout this study no substantial knowledge of machine learning is required from the reader. This makes this work also double up as a tutorial for researchers aiming at applying these algorithms.