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A machine learning strategy for computing interface curvature in Front-Tracking methods
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DOI：10.1016/j.jcp.2021.110860.png)
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摘要
En 中文
In this work we have described the application of a machine learning strategy to compute the interface curvature in the context of a Front-Tracking framework. Based on angular information of normal and tangential vectors between marker points, the interface curvature is predicted using a neural network. The Front-Tracking-Machine-Learning method is validated using a sine wave and then applied in combination with a Marker-And-Cell method for solving a complex free surface flow. Our results indicate that it is feasible to employ machine learning concepts as an alternative approach for computing curvatures in Front-Tracking schemes. (C) 2021 The Authors. Published by Elsevier Inc.
Keyword:
Machine learning
Front-Tracking
Curvature
Free surface flows
Marker-and-cell
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期刊
IF:
3.8
论文数:
1.6W
被引数:
7.4W
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