Computer Physics Communications最新文献

{"title":"Directional finite difference method for directly solving 3D gyrokinetic field equations with enhanced accuracy","authors":"Min-Gu Yoo ,&nbsp;Weixing Wang ,&nbsp;Edward Startsev ,&nbsp;Stephane Either","doi":"10.1016/j.cpc.2025.109597","DOIUrl":"10.1016/j.cpc.2025.109597","url":null,"abstract":"<div><div>The gyrokinetic (GK) field equation is a three-dimensional (3D) elliptic equation, but it is often simplified to a set of two-dimensional (2D) equations by assuming that the field does not vary along a specific direction. However, this simplification can introduce inevitable 0th-order numerical errors, as nonlinear mode coupling in toroidal geometry can produce undesirable harmonic modes that violate the assumption. In this work, we propose a novel directional finite difference method (FDM) with a local coordinate transformation to better resolve the target field of interest. The directional FDM can accurately solve 3D GK field equations without simplifications, which can overcome the limitations of conventional methods. The accuracy and efficiency of different FDMs are analyzed in great detail for a variety of geometries, from simple 2D Cartesian coordinates to realistic 3D curvilinear coordinates. The 0th-order numerical errors of simplified 2D GK equations were found to be more problematic for low-harmonic modes and low aspect ratio geometries such as spherical tokamaks. On the other hand, the directional 3D FDM can accurately resolve a much wider range of harmonic modes aligned to the direction of interest, including the low-harmonic modes. We demonstrate that the directional 3D FDM is a highly effective algorithm for solving the 3D GK field equations, achieving accuracy improvements of 10 to 100 times or more, particularly for low-harmonic modes in spherical tokamaks.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109597"},"PeriodicalIF":7.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An OpenFOAM-based solver for modeling low Mach number turbulent flows at high pressure with real-fluid effects","authors":"Danh Nam Nguyen ,&nbsp;Chun Sang Yoo","doi":"10.1016/j.cpc.2025.109600","DOIUrl":"10.1016/j.cpc.2025.109600","url":null,"abstract":"<div><div>Numerical simulations of non-reacting/reacting flows at supercritical pressure near the critical points with real-fluid models in OpenFOAM often encounter instability and divergence issues unless the solution algorithm incorporates special techniques. In this paper, we develop a novel pressure-based solver, <em>realFluidFoam</em>, tailored for simulations of subsonic turbulent flows at transcritical and supercritical conditions in OpenFOAM. The <em>realFluidFoam</em> solver utilizes unique algorithms to enhance the stability and convergency while taking into account real-fluid effects. Its source code and implementation details are provided to facilitate a comprehensive understanding of integrating real-fluid models into fluid flow simulations in OpenFOAM. The <em>realFluidFoam</em> solver is validated against experimental data by performing large-eddy simulations (LESs) of liquid nitrogen injection and coaxial liquid nitrogen/preheated hydrogen injection under transcritical and supercritical conditions. The LES results show a satisfactory agreement with the experimental data, verifying that the <em>realFluidFoam</em> solver can accurately simulate transcritical and supercritical turbulent fluid flows over the wide range of pressure, especially near the critical points.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> realFluidFoam</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/btzj8b7b8j.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link (OpenFOAM-6 ver.):</em> <span><span>https://github.com/danhnam11/realFluidFoam-6</span><svg><path></path></svg></span></div><div><em>Developer's repository link (OpenFOAM-8 ver.):</em> <span><span>https://github.com/danhnam11/realFluidFoam-8</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> C++</div><div><em>Nature of problem:</em> Instability and divergence problems are often encountered in simulations of subsonic fluid flows under high pressure conditions near critical points (i.e., transcritical and supercritical conditions) using real-fluid models in OpenFOAM due to pseudo-boiling effects and high density stratifications.</div><div><em>Solution method:</em> A pressure-based solution method with a modified PIMPLE algorithm is employed to improve the stability while a fast and robust coupling Newton-Bisection algorithm is utilized to guarantee the convergency of fluid flow simulations under transcritical and supercritical conditions in OpenFOAM.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109600"},"PeriodicalIF":7.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient three-dimensional mesh quality optimization method based on gradient-enhanced probabilistic model","authors":"Longwei Deng ,&nbsp;Junhui Yin ,&nbsp;Qing He ,&nbsp;Xinyu Cao ,&nbsp;Chaoyang Zhang ,&nbsp;Junhao Cui ,&nbsp;Bin Li","doi":"10.1016/j.cpc.2025.109602","DOIUrl":"10.1016/j.cpc.2025.109602","url":null,"abstract":"<div><div>As a cornerstone of numerical simulations, mesh generation establishes the initial discrete model required for simulations, and the quality of the mesh significantly impacts the accuracy of the analysis results. However, the initial mesh elements generated by automated mesh generators often do not meet the stringent requirements of numerical simulations due to their poor mesh quality. This paper proposes an efficient three-dimensional tetrahedral mesh quality optimization method based on gradient-enhanced probabilistic model. The proposed method includes a preprocessing step that first solves for the steepest descent direction and optimal step length of nodes, allowing for the rapid optimization of early node movement and placement, subsequently completing the initial relocation of nodes. By establishing a probabilistic model for determining the optimal node positions and creating a memoryless stochastic process, the method ensures good convergence speed and accuracy as the node positions approach their optimal solutions. Therefore, the proposed method not only accelerates the overall optimization efficiency but also enhances mesh quality, achieving a balanced improvement between smoothing efficiency and mesh quality. This paper validates the proposed method on both three-dimensional tetrahedral meshes and surface meshes, and develops a parallel version, demonstrating the method's broad applicability and strong optimization capability. Through ablation study and comparisons with classic methods, it is shown that the proposed method outperforms traditional methods in both optimization efficiency and mesh quality. The GitHub repository link is: <span><span>https://github.com/suyi-92/EMeshOptimization.git</span><svg><path></path></svg></span>. And the input files can be found at: <span><span>https://drive.google.com/drive/folders/1ziiWzmorx82NiVJPxWI0yoBrPpk_Lzrg?usp=sharing</span><svg><path></path></svg></span></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109602"},"PeriodicalIF":7.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct minimization on the complex Stiefel manifold in Kohn-Sham density functional theory for finite and extended systems","authors":"Kai Luo ,&nbsp;Tingguang Wang ,&nbsp;Xinguo Ren","doi":"10.1016/j.cpc.2025.109596","DOIUrl":"10.1016/j.cpc.2025.109596","url":null,"abstract":"<div><div>Direct minimization method on the complex Stiefel manifold in Kohn-Sham density functional theory is formulated to treat both finite and extended systems in a unified manner. This formulation is well-suited for scenarios where straightforward iterative diagonalization becomes challenging, especially when the Aufbau principle is not applicable. We present the theoretical foundation and numerical implementation of the Riemannian conjugate gradient (RCG) within a localized non-orthogonal basis set. Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) method is tentatively implemented. Extensive testing compares the performance of the proposed methods and highlights that the quasi-Newton method is more efficient. However, for extended systems, the computational time required grows rapidly with respect to the number of <strong>k</strong>-points.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109596"},"PeriodicalIF":7.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepFlame 2.0: A new version for fully GPU-native machine learning accelerated reacting flow simulations under low-Mach conditions","authors":"Runze Mao ,&nbsp;Xinyu Dong ,&nbsp;Xuan Bai ,&nbsp;Ziheng Wu ,&nbsp;Guanlin Dang ,&nbsp;Han Li ,&nbsp;Zhi X. Chen","doi":"10.1016/j.cpc.2025.109595","DOIUrl":"10.1016/j.cpc.2025.109595","url":null,"abstract":"<div><div>This paper presents <em>DeepFlame</em> v2.0, a significant computational framework upgrade designed for high-performance combustion simulations on GPU-based heterogeneous architectures. The updated version implements a comprehensive CUDA-accelerated architecture incorporating fundamental combustion modelling components, including: implicit/explicit finite volume method (FVM) discretisation schemes, chemical kinetics integrators, thermophysical property models, and subgrid-scale closures for both fluid dynamics and combustion processes. The redesigned code supports diverse boundary conditions and discretisation schemes for broad applicability across combustion configurations. Key performance optimisations integrate advanced CUDA features including data coalescing techniques, CUDA Graphs for kernel scheduling, and NCCL-based multi-GPU communication. Validation studies employing the fully-implicit low-Mach solver demonstrate two-order-of-magnitude acceleration compared to conventional CPU implementations across canonical test cases, while maintaining numerical accuracy.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109595"},"PeriodicalIF":7.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QUANTUM ESPRESSO implementation of the RPA-based functional","authors":"Angel Rosado,&nbsp;Mario Benites,&nbsp;Efstratios Manousakis","doi":"10.1016/j.cpc.2025.109594","DOIUrl":"10.1016/j.cpc.2025.109594","url":null,"abstract":"<div><div>We detail our implementation of the random-phase-approximation based functional (RPAF) derived in Ref. <span><span>[1]</span></span> for the QUANTUM ESPRESSO (QE) package. We also make available in the <em>Computer Physics Communications</em> library the source files which are required in order to apply this functional within QE. We also provide the corresponding RPAF projector augmented wave (PAW) and ultrasoft pseudopotentials for most elements. Lastly, we benchmark the performance of the RPAF by calculating the equilibrium lattice constant and bulk modulus of a set of the same 60 crystals used by other authors to benchmark other functionals for both PAW and ultrasoft pseudopotentials. We find that the RPAF performs better overall as compared to the other most popular functionals.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> Implementation of RPAF functional in QUANTUM ESPRESSO</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/y96kpb2dpd.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://data.mendeley.com/datasets/bg45fjkz2t</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> Fortran 90</div><div><em>Nature of problem:</em> To make the RPAF available to be used in DFT calculations.</div><div><em>Solution method:</em> Implementation of RPAF in QUANTUM ESPRESSO.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109594"},"PeriodicalIF":7.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractal-constrained deep learning for super-resolution of turbulence with zero or few label data","authors":"Jiaxin Wu ,&nbsp;Min Luo ,&nbsp;Boo Cheong Khoo ,&nbsp;Dunhui Xiao ,&nbsp;Pengzhi Lin","doi":"10.1016/j.cpc.2025.109548","DOIUrl":"10.1016/j.cpc.2025.109548","url":null,"abstract":"<div><div>The super-resolution of turbulence is of paramount importance and still remains challenging due to the inefficiency of the current technologies in retaining the intrinsic physics like the multi-scale flow structures and energy cascades. To address this challenge, this work proposes a fractal-constrained deep learning super-resolution model termed SKFSR-FIC. The model is characterized by two distinctive designs: (1) a SKip-connected Feature-reuse Super-Resolution (SKFSR) network that learns and retains multi-scale flow structures and multi-frequency dynamics, achieving efficient upscaling of flow fields while reconstructing self-similar physics; (2) a fractal invariance constraint (FIC) that utilizes the self-similarities of flow properties invariant to scales to substitute label data information in super resolution, thus achieving accurate reconstruction of multi-scale dynamics and energy cascades. The SKFSR-FIC model, for the first time, leverages fractal dimensions to guide the turbulent flow reconstruction and significantly reduces the reliance on label data and, especially, achieves zero-shot (i.e., unsupervised) super-resolution that cannot be handled by existing deep learning models. The results from five self-affine fractal images and two turbulent flow cases demonstrate the enhanced efficiency (up to 2 times) and accuracy (up to 100 times) of the unsupervised SKFSR-FIC model compared to the conventional interpolation method and deep learning models. Moreover, the SKFSR network is compatible with both FIC and label data, thereby adaptively enabling unsupervised, supervised and semi-supervised learning strategies. In particular, the semi-supervised SKFSR-FIC model, even by using one snapshot, achieves the best accuracy among the three learning strategies due to the combination of physics and data.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109548"},"PeriodicalIF":7.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid multi-head physics-informed neural network for depth estimation in terahertz imaging","authors":"Mingjun Xiang ,&nbsp;Hui Yuan ,&nbsp;Kai Zhou ,&nbsp;Hartmut G. Roskos","doi":"10.1016/j.cpc.2025.109586","DOIUrl":"10.1016/j.cpc.2025.109586","url":null,"abstract":"<div><div>Terahertz (THz) imaging is a topic in the field of optics, that is intensively investigated not least due to its potential for recording three-dimensional (3D) images, useful e.g., for the detection of hidden objects, nondestructive testing, and radar-like imaging in conjunction with automotive systems. Depth information retrieval is a key factor to recover the three-dimensional shape of objects. Impressive results for depth determination in the visible and infrared spectral range have been demonstrated through deep learning (DL). Among them, most DL methods are merely data-driven, lacking relevant physical priors, which thus requires a large amount of experimental data to train the DL models. However, acquiring large training data in the THz domain is challenging due to the time-consuming data acquisition process and environmental and system stability requirements during this lengthy process. To overcome this limitation, this paper incorporates a complete physical model representing the THz image formation process into a DL neural network(NN). Having addressed phase retrieval and image reconstruction of planar objects in an earlier paper, we focus here on the task to retrieve the distance information of objects. A significant goal of our work is to be able to use the DL NNs without pre-training, eliminating the need for tens of thousands of labeled data. Through experimental validation, we demonstrate that by providing diffraction patterns of planar objects, with their upper and lower halves sequentially masked to overcome the trapping of the NN's computational iterations in local minima, the proposed physics-informed NN can automatically reconstruct the depth of the object through interaction between the NN and the physical model. Compared to traditional DL methods and back-propagation methods, our approach not only reduces data dependency and operational costs but also improves imaging speed and stability. The obtained results also represent the initial steps towards achieving fast holographic THz imaging using reference-free beams and low-cost power detection.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109586"},"PeriodicalIF":7.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning spatiotemporal dynamics from sparse data via a high-order physics-encoded network","authors":"Pu Ren ,&nbsp;Jialin Song ,&nbsp;Chengping Rao ,&nbsp;Qi Wang ,&nbsp;Yike Guo ,&nbsp;Hao Sun ,&nbsp;Yang Liu","doi":"10.1016/j.cpc.2025.109582","DOIUrl":"10.1016/j.cpc.2025.109582","url":null,"abstract":"<div><div>Learning unknown or partially known dynamics has gained significant attention in scientific machine learning (SciML). This research is mainly driven by the inherent sparsity and noise in scientific data, which poses challenges to accurately modeling spatiotemporal systems. While recent physics-informed learning strategies have attempted to address this problem by incorporating physics knowledge as soft constraints, they often encounter optimization and scalability issues. To this end, we present a novel physics-encoded learning framework for capturing the intricate dynamical patterns of spatiotemporal systems from limited sensor measurements. Our approach centers on a deep convolutional-recurrent network, termed Π<span>-block</span>, which hard-encodes known physical laws (e.g., PDE structure and boundary conditions) into the learning architecture. Moreover, the high-order time marching scheme (e.g., Runge-Kutta fourth-order) is introduced to model the temporal evolution. We conduct comprehensive numerical experiments on a variety of complex systems to evaluate our proposed approach against baseline algorithms across two tasks: reconstructing high-fidelity data and identifying unknown system coefficients. We also assess the performance of our method under various noisy levels and using different finite difference kernels. The comparative results demonstrate the superiority, robustness, and stability of our framework in addressing these critical challenges in SciML.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109582"},"PeriodicalIF":7.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Nektar++ spectral/hp element framework for parallel-in-time simulations","authors":"Jacques Y. Xing ,&nbsp;Chris D. Cantwell ,&nbsp;David Moxey","doi":"10.1016/j.cpc.2025.109584","DOIUrl":"10.1016/j.cpc.2025.109584","url":null,"abstract":"<div><div>We describe the efficient implementation of the Parareal algorithm in the <em>Nektar++</em> software, an open-source spectral/hp element framework for the solution of partial differential equations, which has been designed to achieve high-scalability on high-performance computing (HPC) clusters using distributed parallelism. Recently, time-parallel integration techniques are being recognized as a potential solution to further increase concurrency and computational speed-up beyond the limits of strong scaling obtained from a pure spatial domain decomposition. Amongst the various time-parallel approaches proposed in the literature, the Parareal algorithm is a non-intrusive and iterative approach, exploiting a fine and a coarse solvers to achieve time-parallelism, and can be applied to both linear and non-linear problems. We discuss the details of the implementation and discuss the specific techniques used to adapt the code to a time-parallel framework. We demonstrate the application of these methods to multiple linear and non-linear problems provided by the existing <em>Nektar++</em> solvers.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109584"},"PeriodicalIF":7.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}