A data-driven smoothed particle hydrodynamics method for fluids

Jinshuai Bai; Ying Zhou; Charith Rathnayaka; Haifei Zhan; Emilie Sauret; Yuantong Gu

doi:10.1016/j.enganabound.2021.06.029

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A data-driven smoothed particle hydrodynamics method for fluids

Journal article

Peer reviewed

A data-driven smoothed particle hydrodynamics method for fluids

Jinshuai Bai, Ying Zhou, Charith Rathnayaka, Haifei Zhan, Emilie Sauret and Yuantong Gu

Engineering Analysis with Boundary Elements, Vol.132, pp.12-32

2021

DOI: https://doi.org/10.1016/j.enganabound.2021.06.029

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Abstract

Numerical Modelling and Mechanical Characterisation

Computational Fluid Dynamics

Artificial Intelligence and Image Processing not elsewhere classified

Rheology

Data clustering

Data-driven computational mechanics

Hydrodynamics modelling

Smoothed particle hydrodynamics

The rheological properties of emerging novel complex fluids are usually governed by multiple variables, which is challenging for traditional parameterized rheological models in the context of hydrodynamics modelling. In this paper, we propose a novel Data-Driven Smoothed Particle Hydrodynamics (DDSPH) method that, instead of applying the empirical rheological models, utilizes discrete experimental datasets to close the Navier-Stokes equations for hydrodynamics modelling. To this end, a DDSPH solver is introduced to search for the best data points that minimize a distance-based penalty function while satisfying the velocity constraints obtained from the previous timestep. In order to improve the computational efficiency of data retrieval, a large volume of experimental rheological data is pre-sectioned into several labelled subgroups so that the data retrieval can be carried out in a small span of data. The robustness of the proposed method with respect to noisy data is achieved via adding a variable, namely the data probability, to qualify the relevance of data points to the clusters. The convergence and robustness of the proposed DDSPH method are investigated through the examples for both Newtonian and non-Newtonian fluids. The numerical examples have demonstrated that the proposed DDSPH is effective and efficient for both Newtonian and non-Newtonian fluids. The proposed DDSPH will open a new avenue for hydrodynamics modelling though some further studies are required in the future.

Details

Title: A data-driven smoothed particle hydrodynamics method for fluids
Authors: Jinshuai Bai (Author) - Queensland University of Technology
Ying Zhou (Author) - Queensland University of Technology
Charith Rathnayaka (Author) - Queensland University of Technology
Haifei Zhan (Author) - Zhejiang University
Emilie Sauret (Author) - Queensland University of Technology
Yuantong Gu (Author) - Queensland University of Technology
Publication details: Engineering Analysis with Boundary Elements, Vol.132, pp.12-32
Publisher: Elsevier Ltd
DOI: 10.1016/j.enganabound.2021.06.029
ISSN: 1873-197X
Organisation Unit: School of Science, Technology and Engineering; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99560708702621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications

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