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A CNN-supported Lagrangian ISPH model for free surface flow

Zhang, Ningbo, Yan, Shiqiang, Ma, Qingwei, Guo, Xiaohu, Xie, Zhihua ORCID: https://orcid.org/0000-0002-5180-8427 and Zheng, Xing 2023. A CNN-supported Lagrangian ISPH model for free surface flow. Applied Ocean Research 136 , 103587. 10.1016/j.apor.2023.103587

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Abstract

As a popular method for modeling violent free surface flow, the incompressible Smoothed Particle Hydrodynamics (ISPH) based on the Lagrangian formulation has attracted a great attention worldwide. The Lagrangian ISPH solves the unsteady Navier-Stokes and continuity equations using the projection method, in which the pressure is obtained by solving the pressure Poisson's equation (PPE) that is the most time-consuming part in the ISPH procedure. In this paper, the Convolutional Neural Network (CNN) is combined with ISPH and used to predict the fluid pressure instead of solving the PPE directly. Although limited attempts of using CNN for solving the PPE in Eulerian formulation (referred to as the Eulerian CNN framework) in mesh-based methods are found in the public domain, the present model is the first ISPH model supported by CNN in a Lagrangian formulation. The proposed model overcome several challenges associated with combining CNN with ISPH, including selecting the input parameters, formulating the objective functions, producing the training dataset and dealing with boundary conditions. Two classic free surface problems, i.e. the dam breaking and the wave propagation, are simulated to evaluate the performance of the present model. Quantitative assessments of the numerical error in terms of both the free surface profile and the pressure field are carried out. The assessments show that the new model does not only give results with satisfactory accuracy, but also requires much less computation time for estimating pressure if the number of particles is large, e.g., 100 thousands particles that is usually required in the practical ISPH simulation for free surface flow.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Advanced Research Computing @ Cardiff (ARCCA)
Engineering
Publisher: Elsevier
ISSN: 0141-1187
Date of First Compliant Deposit: 15 May 2023
Date of Acceptance: 1 May 2023
Last Modified: 13 Jun 2024 16:59
URI: https://orca.cardiff.ac.uk/id/eprint/159307

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