Journal of Computational Physics最新文献

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A simplified fast multipole method based on strong recursive skeletonization

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-30 DOI: 10.1016/j.jcp.2024.113707

Anna Yesypenko , Chao Chen , Per-Gunnar Martinsson

This work introduces a kernel-independent, multilevel, adaptive algorithm for efficiently evaluating a discrete convolution kernel with a given source distribution. The method is based on linear algebraic tools such as low rank approximation and “skeleton representations” to approximate far-field interactions. While this work is related to previous linear algebraic formulations of the fast multipole method, the proposed algorithm is distinguished by relying on simpler data structures.

The proposed algorithm eliminates the need for explicit interaction lists by restructuring computations to operate exclusively on the near-neighbor list at each level of the tree, thereby simplifying both implementation and data structures. This work also introduces novel translation operators that significantly simplify the handling of adaptive point distributions. As a kernel-independent approach, it only requires evaluation of the kernel function, making it easily adaptable to a variety of kernels. By using operations on the neighbor list (of size at most 27 in 3D) rather than the interaction list (of size up to 189 in 3D), the algorithm is particularly well-suited for parallel implementation on modern hardware.

Numerical experiments on uniform and non-uniform point distributions in 2D and 3D demonstrate the effectiveness of the proposed parallel algorithm for Laplace and (low-frequency) Helmholtz kernels. The algorithm constructs a tailored skeleton representation for the given geometry during a precomputation stage. After precomputation, the fast summation achieves high efficiency on the GPU using batched linear algebra operations.

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引用次数: 0

Physics-informed data-driven cavitation model for a specific Mie–Grüneisen equation of state

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-30 DOI: 10.1016/j.jcp.2024.113703

Minsheng Huang , Chengbao Yao , Pan Wang , Lidong Cheng , Wenjun Ying

Unsteady cavitation, as observed in phenomena like underwater explosions, entails dynamically evolving boundaries and developing dimensions of cavitation before collapse. The classic one-fluid models often fail to accurately simulate this, as they either rely solely on physics or experimental data. In this study, we introduce a novel one-fluid cavitation model tailored for a specific Mie-Grüneisen equation of state (EOS) known as polynomial EOS, employing an artificial neural network. Our approach integrates physics-informed equations with experimental data through an optimization problem. This physics-informed data-driven model accurately predicts pressure within the cavitation region, where pressure tends towards zero along with density. We apply this model to complex compressible multi-phase flow simulations, including nuclear and underwater explosions. Validation against experimental data and comparison with existing models precedes its application in one- and two-dimensional cases. The simulation of three-dimensional cavitation phenomena near submarines during underwater explosions yields a high-quality numerical solution. The outcome underscores the significant engineering value of the novel cavitation model.

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引用次数: 0

A new flow dynamic approach for Wasserstein gradient flows

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-27 DOI: 10.1016/j.jcp.2024.113696

Qing Cheng , Qianqian Liu , Wenbin Chen , Jie Shen

We develop in this paper a new regularized flow dynamic approach to construct efficient numerical schemes for Wasserstein gradient flows in Lagrangian coordinates. Instead of approximating the Wasserstein distance which needs to solve constrained minimization problems, we reformulate the problem using the Benamou-Brenier's flow dynamic approach, leading to algorithms which only need to solve unconstrained minimization problem in

L^{2}

distance. Our schemes automatically inherit some essential properties of Wasserstein gradient systems such as positivity-preserving, mass conservative and energy dissipation. We present ample numerical simulations of Porous-Medium equations, Keller-Segel equations and Aggregation equations to validate the accuracy and stability of the proposed schemes. Compared to numerical schemes in Eulerian coordinates, our new schemes can capture sharp interfaces for various Wasserstein gradient flows using relatively smaller number of unknowns.

引用次数: 0

Diffuse-interface modeling and energy-stable numerical framework for the heat transfer-coupled two-phase fluids in contact with solids

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-27 DOI: 10.1016/j.jcp.2024.113699

Fang Zhu , Keyue Sun , Guangtao Zhang , Junxiang Yang

To efficiently simulate the heat transfer-coupled two-phase fluid flows with wetting condition in irregular domains, we develop a diffuse-interface heat fluid system. A traditional ternary Cahn–Hilliard model is modified to approximate the damping of solid on fluid. Based on equilibrium interface assumption and Young's equality, an extra term reflecting wetting contact line is derived and added into the diffuse-interface model. The heat transfer and fluid dynamics are described by coupling the penalized incompressible Navier–Stokes equations and a diffusion conduction equation with variable coefficients. The proposed model can efficiently describe complex heat fluid flows in contact with solids because the computations are implemented in regular rectangular domains. The complex techniques for the treatment of fluid-solid boundary are not necessary. Moreover, the proposed model also leads to an energy dissipation law. To satisfy this basic physical property in simulation, we propose linear, totally decoupled, and second-order energy-stable scheme to update the solutions. The time-discretized energy law is analytically estimated. In each time step, the solutions can be easily obtained by solving several linear elliptic-type equations in a step-by-step manner. Extensive numerical experiments in two- and three-dimensional spaces are implemented to validate the accuracy and stability of our method. These results also indicate that the proposed method has good potential in simulating complex fluid flows with heat transfer.

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引用次数: 0

Improved gas kinetic flux solver with locally rescaled Gauss-Hermite quadrature for supersonic continuum and rarefied flows

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-27 DOI: 10.1016/j.jcp.2024.113700

Zhe Li

This paper presents an improved Gas Kinetic Flux Solver (GKFS) for simulating supersonic gas flows in both continuum and rarefied regimes. The key of the improvement lies in the use of a rescaled Gauss-Hermite quadrature in particle velocity space, leading to an adaptive velocity strategy in the numerical quadratures for computing the macroscopic variables and the normal flux at each cell interface. This strategy adjusts the quadrature points according to the local flow characteristics, enhancing the solver's ability to handle supersonic continuum and moderately rarefied flows in an efficient way. Numerical simulations of several 1D shock wave test-cases show that fewer quadrature points are need to achieve a good accuracy, comparing with the analytical solution. The improved GKFS has proven capable of simulating 2D oblique shock wave problems, supersonic rarefied flows round the circular cylinder and the NACA0012 airfoil test-cases, with an excellent agreement with the references.

引用次数: 0

RF-PINNs: Reactive flow physics-informed neural networks for field reconstruction of laminar and turbulent flames using sparse data

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-27 DOI: 10.1016/j.jcp.2024.113698

Vikas Yadav, Mario Casel, Abdulla Ghani

Physics-Informed Neural Networks (PINNs) have emerged as a promising tool to model flow fields by embedding physical laws into neural networks and thereby reducing the dependency on data. While PINNs have shown substantial success in modeling non-reactive flows, their application for chemically reacting flows is less well understood. The overarching objective of this work is to propose reactive flow physics-informed neural networks (RF-PINNs) that reconstruct all flow quantities of interest solely based on sparse velocity profiles. We present RF-PINNs to reconstruct steady-state mean fields of premixed laminar and turbulent flames with sparse data. First, we extensively study a prototype flame to determine the best combination of governing equations to find a balance between prediction accuracy and optimization speed. We then demonstrate the RF-PINN prediction capabilities by utilizing measured velocity profiles to reconstruct the entire velocity, temperature, and density mean fields of laminar and turbulent flames. To highlight the bidirectional reconstruction capability, we utilize in the second step measured temperature profiles for both flames and, again, successfully reconstruct the flow fields of interest. For both scenarios, the subsequent comparison of predicted field quantities based on only 0.2% of the entire data set is sufficient for accurate field reconstruction. This study underscores the potential of RF-PINNs to complement comprehensive field data of laminar and turbulent reacting flows using both sparse and noisy experimental data.

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引用次数: 0

Approximately pressure-equilibrium-preserving scheme for fully conservative simulations of compressible multi-species and real-fluid interfacial flows

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-27 DOI: 10.1016/j.jcp.2024.113701

H. Terashima , N. Ly , M. Ihme

This study proposes a numerical method for fluid interfaces in compressible multi-species and real-fluid flow simulations. The proposed method preserves the full conservation (species-mass, momentum, and energy) property of compressible flow equations while approximately maintaining the pressure equilibrium condition at fluid interfaces. The numerical fluxes of internal energy and species-mass are newly constructed to satisfy the pressure equilibrium condition approximately. The modified equation for the pressure equilibrium condition shows that the proposed numerical fluxes introduce different coefficients in the second-order error term, compared to standard numerical fluxes, thereby reducing the pressure equilibrium error. The conservation and pressure equilibrium properties of the proposed method are validated through one-dimensional and two-dimensional smooth interface advection problems using the compressible multi-species Euler equations with the Soave-Redlich-Kwong equation of state.

引用次数: 0

Structure preserving hybrid Finite Volume Finite Element method for compressible MHD

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-20 DOI: 10.1016/j.jcp.2024.113691

Francesco Fambri , Eric Sonnendrücker

In this manuscript we present a novel and efficient numerical method for the compressible viscous and resistive MHD equations for all Mach number regimes. The time-integration strategy is a semi-implicit splitting, combined with a hybrid Finite Volume and Finite Element (FE) discretization in space. The nonlinear convection is solved by a robust explicit FV scheme, while the magneto-acoustic terms are treated implicitly in time. The resulting CFL stability condition depends only on the fluid velocity, and not on the Alfvénic and acoustic modes. The magneto-acoustic terms are discretized by compatible FE based on a continuous and a discrete de Rham complexes designed using Finite Element Exterior Calculus (FEEC). Thanks to the use of FEEC, energy stability, magnetic-helicity conservation and the divergence-free conditions can be preserved also at the discrete level. A very efficient splitting approach is used to separate the acoustic and the Alfvénic modes in such a fashion that the original symmetries of the PDE governing equations are preserved. In this way, the algorithm relies on the solution of linear, symmetric and positive-definite algebraic systems, that are very efficiently handled by the simple matrix-free conjugate-gradient method. The resulting algorithm showed to be robust and accurate in low and high Mach regimes even at large Courant numbers. Non-trivial tests are solved in one-, two- and three-space dimensions to confirm the robustness, accuracy, and the low-dissipative and conserving properties of the final algorithm. While the formulation of the method is very general, numerical results for a second-order accurate FV-FE scheme will be presented.

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引用次数: 0

An immersed boundary method using online sequential data assimilation

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-20 DOI: 10.1016/j.jcp.2024.113697

Miguel M. Valero, Marcello Meldi

A data-driven strategy relying on the Ensemble Kalman Filter (EnKF) is here used to augment the accuracy of a continuous Immersed Boundary Method (IBM). The latter is a classical penalty method accounting for the presence of the immersed body via a volume source term, which is included in the Navier–Stokes equations. The model coefficients of the penalisation method, which are usually selected by the user, are optimised using the data-driven strategy. The parametric inference is governed by the physical knowledge of local and global features of the flow, such as the no-slip condition and the shear stress at the wall. The C++ library CONES (Coupling OpenFOAM with Numerical EnvironmentS) developed by the team is used to perform an online investigation, coupling on-the-fly data from synthetic sensors with results from an ensemble of coarse-grained numerical simulations. The analysis is performed for a classical test case, namely the turbulent plane channel flow with

R e_{τ} = 550

. The results, which are compared with high-fidelity Direct Numerical Simulation (DNS), show that the data-driven procedure exhibits remarkable accuracy despite the ensemble members' relatively low grid resolution. The findings present open perspectives of application in dynamic complex systems, such as digital twins.

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引用次数: 0

Novel local characteristic decomposition based path-conservative central-upwind schemes

IF 3.8 2区物理与天体物理 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Physics

Pub Date : 2024-12-20 DOI: 10.1016/j.jcp.2024.113692

Shaoshuai Chu , Michael Herty , Alexander Kurganov

We introduce local characteristic decomposition based path-conservative central-upwind schemes for (nonconservative) hyperbolic systems of balance laws. The proposed schemes are made to be well-balanced via a flux globalization approach, in which source terms are incorporated into the fluxes: This helps to enforce the well-balanced property when the resulting quasi-conservative system is solved using the local characteristic decomposition based central-upwind scheme recently introduced in [A. Chertock, S. Chu, M. Herty, A. Kurganov, and M. Lukáčová-Medvi

ová, J. Comput. Phys., 473 (2023), Paper No. 111718]. Nonconservative product terms are also incorporated into the global fluxes using a path-conservative technique. We illustrate the performance of the developed schemes by applying them to one- and two-dimensional compressible multifluid systems and thermal rotating shallow water equations.

引用次数: 0

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