A complex network framework for studying particle-laden flows
Physics of Fluids, ISSN: 1089-7666, Vol: 34, Issue: 7
2022
- 4Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Studying particle-laden flows is essential for understanding diverse physical processes such as rain formation in clouds, pathogen transmission, and pollutant dispersal. This work introduces a framework of complex networks to analyze the particle dynamics through a Lagrangian perspective. To illustrate this method, we study the clustering of inertial particles (small heavy particles) in Taylor-Green flow, where the dynamics depend on the particle Stokes number (St). Using complex networks, we can obtain the instantaneous local and global clustering characteristics simultaneously. Furthermore, from the complex networks derived from the particle locations, we observe an emergence of a giant component through a continuous phase transition as particles cluster in the flow field, thus providing novel insight into the spatiotemporal dynamics of particles such as the rate of clustering. Finally, we believe that complex networks have a great potential for analyzing the spatiotemporal dynamics of particle-laden flows.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85135030812&origin=inward; http://dx.doi.org/10.1063/5.0098917; https://pubs.aip.org/aip/pof/article-lookup/doi/10.1063/5.0098917; http://dx.doi.org/10.1063/5.0098917.1; http://dx.doi.org/10.1063/5.0098917.3; http://dx.doi.org/10.1063/5.0098917.2; https://pubs.aip.org/pof/article/34/7/073321/2846920/A-complex-network-framework-for-studying-particle
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