Numerical analyses of liquid slosh by Finite volume and Lattice Boltzmann methods

Yang, Chen; Niu, Ran; Zhang, Peng

doi:10.1016/j.ast.2021.106681

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…The field of fluid mechanics includes numerous partial differential equations (PDEs) essential for studying fluid dynamics. Computational fluid dynamics (CFD) uses numerical analysis and data structures to analyze and solve fluid flow problems, incorporating various numerical solving methods such as the finite element method [ 1 ], finite volume method [ 2 ], and spectral methods [ 3 ]. Despite significant advancements over recent decades, these methods often face complexities due to mesh division in practical applications.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information

Liu,

Chen,

Chen

et al. 2024

Entropy

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Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress of adaptive sampling techniques, addressing the inadequacy of existing algorithms to fully leverage the spatial location information of sample points, and introduces an innovative adaptive sampling method. This approach incorporates the Dual Inverse Distance Weighting (DIDW) algorithm, embedding the spatial characteristics of sampling points within the probability sampling process. Furthermore, it introduces reward factors derived from reinforcement learning principles to dynamically refine the probability sampling formula. This strategy more effectively captures the essential characteristics of PDEs with each iteration. We utilize sparsely connected networks and have adjusted the sampling process, which has proven to effectively reduce the training time. In numerical experiments on fluid mechanics problems, such as the two-dimensional Burgers’ equation with sharp solutions, pipe flow, flow around a circular cylinder, lid-driven cavity flow, and Kovasznay flow, our proposed adaptive sampling algorithm markedly enhances accuracy over conventional PINN methods, validating the algorithm’s efficacy.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information

Liu,

Chen,

Chen

et al. 2024

Entropy

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“…Ünal et al [15] applied FDM to investigate the liquid sloshing in a closed, partially filled, T-shaped baffled and unbaffled two-dimensional rectangular tank, the results imply that the baffle is fully effective in pressure and wave damping when its height is greater than 80% of the liquid level. Yang et al [16] used combined FVM method and Lattice Boltzmann method to perform the quick evaluation of liquid sloshing behavior under different container designs, the results show that the computational time cost of LBM model is only around 7.0% of the time cost for FVM model without compromise of the numerical accuracy. Cho et al [17] applied BEM to obtain the analytic solution for the sloshing with porous horizontal baffle, two baffle positions at the center and at both walls of a rectangular tank were considered for various porosities, lengths, and submergence depths.…”

Section: Introductionmentioning

confidence: 99%

Anti-slosh effect of baffle configurations and air pressure on liquid sloshing in partially filled tank trucks

Wang,

Ping,

et al. 2024

Meas. Sci. Technol.

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The phenomenon of liquid sloshing inside partially filled tank trucks adversely affects the stability of the tanktrucks. In order to mitigate the negative effects of liquid sloshing inside the tank, this study proposes several baffle configurations and investigates their anti-slosh effect on liquid sloshing. First, a numerical model of liquid sloshing is established. Then, the effectiveness of the numerical model is validated by comparing the results of free surface deformation, wall pressure, and sloshing frequency obtained from simulations and experiments under identical conditions. During the research process, it was found that the air pressure formed in locally sealed spaces within the tank also plays a positive role in suppressing liquid sloshing. The research results indicate that, under low fill volumes, baffles fixed at the bottom of the tank are more effective in suppressing liquid sloshing inside the tank, while under high fill volumes, baffles fixed at the top of the tank are more effective. Considering the tank’s airtightness, the air pressure formed in locally sealed spaces within the tank plays an important role in suppressing liquid sloshing when baffles are fixed at the top of the tank and the fill volume is high.

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“…In physics and engineering, many important physical models are described as partial differential equations (PDEs), such as Navier–Stokes equations [ 4 ] for fluid mechanics and Maxwell equations [ 5 ] for electromagnetic field theory. When solving partial differential equations using traditional numerical methods, such as the Finite Difference Method (FDM) [ 6 ], Finite Element Method (FEM) [ 7 ], Finite Volume Method (FVM) [ 8 ], Radial Basis Function Method (RBF) [ 9 ], etc., problems such as high computational costs and the curse of dimensionality are often encountered. Over the last few years, the use of machine learning to solve partial differential equations has also rapidly expanded [ 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Second-Order Network Structure Based on Gradient-Enhanced Physics-Informed Neural Networks for Solving Parabolic Partial Differential Equations

Sun

Feng

2023

Entropy

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Physics-informed neural networks (PINNs) are effective for solving partial differential equations (PDEs). This method of embedding partial differential equations and their initial boundary conditions into the loss functions of neural networks has successfully solved forward and inverse PDE problems. In this study, we considered a parametric light wave equation, discretized it using the central difference, and, through this difference scheme, constructed a new neural network structure named the second-order neural network structure. Additionally, we used the adaptive activation function strategy and gradient-enhanced strategy to improve the performance of the neural network and used the deep mixed residual method (MIM) to reduce the high computational cost caused by the enhanced gradient. At the end of this paper, we give some numerical examples of nonlinear parabolic partial differential equations to verify the effectiveness of the method.

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Numerical analyses of liquid slosh by Finite volume and Lattice Boltzmann methods

Cited by 17 publications

References 31 publications

An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information

An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information

Anti-slosh effect of baffle configurations and air pressure on liquid sloshing in partially filled tank trucks

A Second-Order Network Structure Based on Gradient-Enhanced Physics-Informed Neural Networks for Solving Parabolic Partial Differential Equations

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