☆ 4.7 Article

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

CHEMICAL ENGINEERING JOURNAL (2022)

Journal

CHEMICAL ENGINEERING JOURNAL

Volume 438, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA

DOI: 10.1016/j.cej.2022.135250

Keywords

Machine learning; Dimensionality reduction; Clustering; Reactive flows

Funding

Formas (Swedish Research Coun-cil for Sustainable Development)
Swedish National Infrastructure for Computing (SNIC) at LUNARC (Lund University) [SNIC 2019/3-641]
PDC Center for High-Performance Computing (KTH Royal Institute of Technology) [SNIC 2019/1-41]

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Abstract

This study examines the capabilities of a data-driven workflow for automated key feature identification in reactive flows. The proposed workflow aims to accelerate the analysis of chemical engineering datasets by generating automatic and explainable classification results for regions with distinct physics. The three main steps of the workflow, namely dimensionality reduction, unsupervised clustering, and feature correlation, are discussed. The study demonstrates the theoretical and practical differences between the previous and current algorithms used in the workflow. The updated workflow is shown to have faster, more accurate, and more robust key feature identification capabilities, closer to human intuition than previous methods. The study also serves as a tutorial for researchers interested in applying these algorithms.

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.

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Machine learning for fluid flow reconstruction from limited measurements

Pierre Dubois, Thomas Gomez, Laurent Planckaert, Laurent Perret

Summary: This paper investigates the use of data-driven methods for the reconstruction of unsteady fluid flow fields. The proposed framework is based on the combination of machine learning tools: dimensionality reduction to extract dominant spatial directions from data, reconstruction algorithm to recover encoded data by limited measurements and cross-validation for hyperparameter optimization. The results suggest that proper machine learning approaches to fluid flow data can lead to effective reconstruction models that can be used for the rapid estimation of complex flows.

JOURNAL OF COMPUTATIONAL PHYSICS (2022)