Computer Physics Communications最新文献

The high-order shock-capturing scheme is one of the main building blocks for the simulation of the compressible fluid characterized by strong shockwaves and broadband length scales. However, the classical shock-capturing scheme fails to perform long-time stable and non-dissipative simulations since the quadratic invariants associated with the conservation equations cannot be conserved as a result of the inherent numerical dissipation. Additionally, the overall computational cost for classical shock-capturing schemes is quite expensive as a result of the time-consuming local characteristic decomposition and the nonlinear-weights computing process. In this work, based on a new efficient discontinuity indicator, which distinguishes the non-smooth high-wavenumber fluctuations and discontinuities from smooth scales in the wavenumber space, a paradigm of high-order shock-capturing scheme by recasting the non-dissipative skew-symmetric-splitting method with newly optimized dispersion property for smooth flow scales and invoking the nonlinear targeted ENO (TENO) schemes for non-smooth discontinuities is proposed. The resulting TENO-S scheme not only successfully performs long-time stable computations for smooth flows without numerical dissipation, but also recovers the robust shock-capturing capabilities with adaptive numerical dissipation. Without the necessity of parameter tuning case by case, extensive benchmark simulations involving a wide range of flow length scales and strong discontinuities demonstrate that the proposed TENO-S scheme performs significantly better than the straightforward deployment of WENO/TENO-family schemes with better spectral property and higher computational efficiency.

We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in modeling these complex systems. Additionally, this framework allows for the optimization of spin models, which can adjust the parameters of a system by a defined objective function. In order to simulate these models, we adapt the Metropolis-Hastings algorithm to a differentiable programming paradigm, employing batched tensors for simulating spin lattices. This adaptation not only facilitates the integration with existing deep learning tools but also significantly enhances computational speed through parallel processing capabilities, as it can be implemented on different hardware architectures, including GPUs and TPUs.

We present Fast-QSGS, a GPU-accelerated program for granular disordered media generation. Based on vectorization, Fast-QSGS is accelerated by modern GPU thanks to the NumPy-compatible API provided by CuPy. We also introduce a variable growth probability function and seed spacing control to improve the speed and accuracy of the original QSGS method. Computational performance benchmarks are conducted on both consumer-grade and professional-grade GPUs. Generation of disordered media of size 400³ can be completed in 30 s on A100 and 110 s on RTX4060, achieving a speedup of over 400 compared with the serial version. Physical benchmarks on the reconstruction of Fontainebleau sandstone and hydrated cement are conducted. Our results demonstrate that the permeability of the reconstructed Fontainebleau sandstone falls within the range of experimental values. Additionally, the average relative error of the volume fraction of the unhydrated cement and capillary porosity of hydrated cement is 1.9 % and 3.4 % compared with Powers’ law, respectively.

Graphene reinforced metal matrix composites represent a promising class of materials for high-strength surface coatings because of their high strength and ductility. This study reports the application of different interatomic potentials to correctly describe the interaction between graphene and metals (Al, Cu, Ni, and Ti) by molecular dynamics. Both simple pair potentials, such as Lennard-Jones and Morse, and many-body potentials, such as bond order potential are applied for the simulation of a graphene/metal system at room temperature. Three different structures are considered: (i) graphene interacting with one metal atom; (ii) graphene interacting with a metal nanoparticle, and (iii) three-dimensional graphene network filled with metal nanoparticles. We first determine the potential energy that any graphene/metal system can reach during exposure at 300 K; then, we analyze the interaction dynamics for all considered systems and all potentials. A considerable difference in the interaction between metal nanoparticles with planar and folded graphene was found. For graphene/Ni, graphene/Cu, and graphene/Ti, the Lennard-Jones and Morse potentials provide accurate energetic and structural properties of the studied structures; they also describe interaction in the graphene/metal system in a similar way, at variance with bond-order potential. For graphene/Al, the Tersoff and Morse potentials describe the interaction better than Lennard-Jones. For the simulation of graphene/Me system, the optimal choice of the potential for different structures is of crucial importance. The presented analysis of the interatomic potentials appears to be promising for realistic and accurate simulations of graphene reinforced metal composites.

This paper is devoted to fourth order compact schemes and fast algorithms for solving stationary Stokes equations with different boundary conditions numerically. One of the main ideas is to decouple the Stokes equations into three Poisson equations for the pressure and the velocity via the pressure Poisson equation (PPE). The augmented strategy is utilized to provide numerical boundary conditions for the pressure. Different velocity boundary conditions require different interpolation strategies for the augmented methods. The augmented variable is solved by the GMRES method. A new simple and efficient preconditioning strategy has also been developed to accelerate the convergence of the GMRES iteration. Numerical examples presented in this paper confirmed the designed convergence order and the efficiency of the new methods.

We present an object-oriented programming (OOP) CUDA-based package for fast and accurate simulation of second-harmonic generation (SHG) efficiency using focused Gaussian beams. The model includes linear as well as two-photon absorption that can ultimately lead to thermal lensing due to self-heating effects. Our approach speeds up calculations by nearly 40x (11x) without (with) temperature profiles with respect to an equivalent implementation using CPU. The package offers a valuable tool for experimental design and study of 3D field propagation in nonlinear three-wave interactions. It is useful for optimization of SHG-based experiments and mitigates undesired thermal effects, enabling improved oven designs and advanced device architectures, leading to stable, efficient high-power SHG.

Program summary

Program Title: cuSHG

CPC Library link to program files: https://doi.org/10.17632/hn76s7x848.1

Developer's repository link: https://github.com/alfredos84/cuSHG

Licensing provisions: MIT

Programming language:

Nature of problem: The problem which is solved in this work is that of second-harmonic generation (SHG) performance degradation in a nonlinear crystal with focused Gaussian beams due to thermal effects. By placing the nonlinear crystal in an oven that controls temperature, the package computes the involved electric fields along the medium. The implemented model includes the linear and nonlinear absorption which occasionally lead to self-heating effect, degrading the performance of the SHG.

Solution method: The coupled differential equations for three-wave interactions, which describe the field evolution along the crystal, are solved using the well-known Split-Step Fourier method. The temperature profiles are estimated using the finite-elements method. The field evolution and thermal effects are embedded in a self-consistent algorithm that sequentially and separately solves the electromagnetic and thermal problems until the system reaches the steady state. Due to the eventual computational demand that some problems may have, we chose to implement the coupled equations in the