A machine learning strategy for computing interface curvature in Front-Tracking methods

Author(s)

Franca, Hugo L. ; Oishi, Cassio M.

• Document type: Journal article
• Source: Journal of Computational Physics; v. 450, p. 9-pg., 2022-02-01.

Abstract

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. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/01811-9 - Numerical simulation of free surface complex flows
• Grantee: Hugo Leonardo França
• Support Opportunities: Scholarships in Brazil - Doctorate