6 results on '"Yan, Shiqiang"'
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2. Numerical simulation of wave-floater interactions using ISPH_GNN trained on data for wave-only cases.
- Author
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Zhang, Ningbo, Yan, Shiqiang, Ma, Qingwei, and Li, Qian
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GRAPH neural networks , *POISSON'S equation , *COMPUTATIONAL fluid dynamics , *COMPUTER simulation , *MACHINE learning - Abstract
As a mesh-free approach, the incompressible Smoothed Particle Hydrodynamics (ISPH) method has been often used for simulating wave-structure interaction problems. In the conventional ISPH method, the pressure-projection phase of solving the pressure Poisson's equation (PPE) is the most time-consuming. In recent years, the machine learning (ML) techniques has gradually shown their potential in accelerating the computational fluid dynamics. In this paper, the graph neural network (GNN) supported ISPH method (ISPH_GNN), in which the GNN replaces solving the PPE for the fluid pressure in the conventional ISPH, is adopted for numerical simulations of wave-floater interactions. To the best of the authors' knowledge, this is the first work to study the wave-floater interactions by using GNN supported ISPH method. More importantly, this paper demonstrates that the GNN trained only on data for simpler wave-only cases (i.e. no structure in the wave fields) can be satisfactorily applied to the cases for wave-floater interactions. More specifically, the paper will show this by using the ISPH_GNN with such trained GNN model to simulate various different cases, including the decay tests of a box, a floating box subjected to a wave, the interaction between wave and a moored floating breakwater and the violent green water impact on a floating structure. In most of the cases, the numerical results are validated by comparing with experimental data. Agreement between them is surprisingly satisfactory, being as good as those obtained by the conventional ISPH. The paper will also show that the ISPH_GNN requires much less computational time (97 times less for the cases concerned) than the conventional ISPH for estimating pressure involved in wave-floater interactions. This reveals a great potential that one can train the GNN using the datasets for simpler cases and then use the ISPH_GNN to simulate wave-floater interaction problems. • The first work to study the wave-floater interactions by using the graph neural network (GNN) supported ISPH method. • The GNN trained only on data for simpler wave-only cases can be applied to more complex cases for wave-floater interactions. • The ISPH_GNN can give satisfactory results for the relatively complex cases involving wave-floater interactions. • The ISPH_GNN takes much less computation time than the conventional ISPH for estimating pressure. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. A hybrid method combining ISPH with graph neural network for simulating free-surface flows.
- Author
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Zhang, Ningbo, Yan, Shiqiang, Ma, Qingwei, and Li, Qian
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GRAPH neural networks , *OPEN-channel flow , *POISSON'S equation , *FLOW simulations , *THEORY of wave motion , *FREE surfaces - Abstract
• The first paper on a hybrid method combining ISPH with graph neural network (ISPH_GNN) for simulating free-surface flows. • The ISPH_GNN gives satisfactory results compared with experimental data, analytical solution and other ISPH results. • Investigation of the potential of generalization of the ISPH_GNN by applying it to simulating more complex scenarios. • The ISPH_GNN can yield satisfactory results for more complex cases beyond the simple cases of training and testing data. • The ISPH_GNN takes much less computation time than the conventional ISPH for pressure prediction. The incompressible Smoothed Particle Hydrodynamics (ISPH) is a popular Lagrangian Particle method. In the conventional ISPH method for simulating free-surface flows, the pressure-projection phase, which solves the pressure Poisson's equation (PPE), is the most time-consuming. In this paper, we propose a novel hybrid method by combining the graph neural network (GNN) with the ISPH for modelling the free-surface flows. In the new hybrid method, the graph neural network (GNN) is employed to replace solving the PPE for pressure in the conventional ISPH. To the best of knowledge of the authors, this is the first attempt to combine the GNN with ISPH model in a Lagrangian formulation. The performance of the hybrid method will be evaluated by comparing its results with experimental data, analytical solution or numerical results from other methods for three benchmark test cases: dam breaking, sloshing wave and solitary wave propagation. In addition, the potential of generalization of the hybrid method will be studied by applying it with the GNN model trained on data for relatively simple cases to simulate more complex cases. It will be demonstrated that the hybrid method does not only give satisfactory results, but also shows good potential of generalization. In addition, the new method will be demonstrated to require the computation time which can be 80 times less than the conventional ISPH for estimating pressure for cases with a large number of particles that is usually needed in the practical free-surface flows simulation using ISPH. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. A consistent second order ISPH for free surface flow.
- Author
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Zhang, Ningbo, Yan, Shiqiang, Ma, Qingwei, Khayyer, Abbas, Guo, Xiaohu, and Zheng, Xing
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FREE surfaces , *SLOSHING (Hydrodynamics) , *POISSON'S equation , *LAPLACIAN operator , *THEORY of wave motion , *QUADRICS - Abstract
• Develop a consistent second order ISPH model (ISPH_CQ) for solving the free surface problems, in which all the Laplacian and gradient operations are directly discretised by the quadric semi-analytical finite difference interpolation scheme (QSFDI). • This is the first ISPH model demonstrating a second order convergence for solving the pressure Poisson's equation in a blended form consisting of both the velocity divergence and density variation term. • Carry out a systematic comparative study on the accuracy, convergence, robustness, mass/energy conservation and the capacity on reproducing the pressure field using various cases with free surface flows, including oscillating liquid drop, wave propagations and liquid sloshing. The Incompressible Smoothed Particle Hydrodynamics (ISPH) is now a popular numerical method for modelling free surface flows, in particular the breaking waves and violent wave-structures interaction. The ISPH requires the projection approach, leading to solving a pressure Poisson's equation (PPE). Although the accuracy and convergence of the numerical scheme to discretise the Laplacian operator involved in PPE is critical for securing a satisfactory solution of the PPE, the overall performance of the ISPH is also influenced by other key numerical implementations, including (1) estimation of the viscous terms; (2) calculation of the velocity divergence; (3) discretisation of the boundary conditions for the PPE; and (4) evaluation of the pressure gradient. In our previous paper [29] , the quadratic semi-analytical finite difference interpolation scheme (QSFDI), which has a leading truncation error at third order derivatives, has been adopted to discretise the Laplacian operator. In this paper, the QSFDI will be adopted, not only for discretising the Laplacian operator, but also for approximating viscous terms, velocity divergence, boundary conditions and pressure gradient. The performance of the newly formulated consistent second order ISPH is assessed by various cases including the oscillating liquid drop, the wave propagation, and the liquid sloshing. The results do not only demonstrate a second order convergence over a limited range of conditions and a higher computational efficiency, i.e., requiring less computational time to achieve the same accuracy, but also show a better mass/energy conservation property and capacity of reproducing a smooth pressure field, than other ISPH models considered in this study. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. A CNN-supported Lagrangian ISPH model for free surface flow.
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Zhang, Ningbo, Yan, Shiqiang, Ma, Qingwei, Guo, Xiaohu, Xie, Zhihua, and Zheng, Xing
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CONVOLUTIONAL neural networks , *POISSON'S equation , *THEORY of wave motion , *WATER waves , *NAVIER-Stokes equations - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
6. A QSFDI based Laplacian discretisation for modelling wave-structure interaction using ISPH.
- Author
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Zhang, Ningbo, Yan, Shiqiang, Ma, Qingwei, and Zheng, Xing
- Subjects
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LAPLACIAN operator , *POISSON'S equation , *FINITE differences , *NAVIER-Stokes equations , *THEORY of wave motion - Abstract
The incompressible Smoothed Particle Hydrodynamics (ISPH) is one of the most popular Lagrangian particle methods for modelling wave-structure interactions. It solves the unsteady Navier-Stokes and continuity equations using the projection method, in which solving the pressure Poisson's equation (PPE) plays a critical role. To discretise the Laplacian operator, the quadric semi-analytical finite difference interpolation scheme (QSFDI) has been developed recently and the relevant patch test has demonstrated its superiority over existing schemes at a similar accuracy level in terms of the convergence and robustness. In this paper, the QSFDI is adopted by the ISPH for discretising the Laplacian operator in the PPE. The developed scheme (ISPH_QSFDI) is then applied to various cases with wave propagations and wave impacts on structures. For the purpose of comparison, other Laplacian discretisation schemes, including the classic scheme widely adopted by the ISPH, the CSPM and the CSPH2Γ, have also been considered. Except the Laplacian discretisation, other numerical implementations of the ISPH are kept the same as the classic ISPH. The convergence, accuracy and robustness of these schemes are analysed with reference to either analytical solutions or experimental data. The results demonstrate that the present ISPH_QSFDI leads to more accurate results with the same number of particles and costs less computational time to achieve a specific accuracy, compared with other schemes, although the convergence rate of the ISPH_QSFDI seems to be one-order lower than the theoretical patch test primarily due to the fact that linear schemes are used for the discretisation of the right-hand side of the PPE, the gradient/divergence estimation and the treatment of the boundary conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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