Computer Physics Communications最新文献

{"title":"A simplified conservation flux scheme for gas kinetics based on OpenFOAM framework I: Shakhov model","authors":"Mengbo Zhu ,&nbsp;Jianfeng Chen ,&nbsp;Xiaoqiang Li ,&nbsp;Congshan Zhuo ,&nbsp;Sha Liu ,&nbsp;Chengwen Zhong","doi":"10.1016/j.cpc.2025.109598","DOIUrl":"10.1016/j.cpc.2025.109598","url":null,"abstract":"<div><div>A solver for the Shakhov model equation, founded on dugksFOAM, has been successfully developed. This was achieved through the application of a conservation-type gas kinetic scheme with a simplified interface flux. The process begins with the updating of macroscopic quantities. Subsequently, the distribution function is computed using these newly updated values. This innovative approach effectively mitigates errors that might occur during the integration of the distribution function, especially when an unstructured velocity space is employed. The solver offers two distinct methods for velocity space integration. The first is a traditional structured space, which can be conveniently adjusted and configured via input files. The second is an unstructured space, which utilizes fewer discrete velocity points. These points are determined based on the mesh files provided by the user. In this unstructured approach, the velocity points are strategically positioned to strike an optimal balance between computing efficiency and precision, thereby enhancing the overall performance and accuracy of the solver.</div><div>The solver's hybrid parallelization technique, specifically the X-space parallelization approach that encompasses both physical and velocity spaces, empowers the efficient execution of large-scale three-dimensional simulations. By subjecting the solver to benchmark cases such as shock tube problems, lid-driven cavity flow, Poiseuille flow, and flows past cylinders, sphere and X-38 vehicle, the accuracy and dependability of this solver have been thoroughly validated and verified. This comprehensive verification process not only benchmark cases the solver's robustness in handling diverse fluid dynamics scenarios but also highlights its potential for broader applications in the field of computational fluid dynamics.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109598"},"PeriodicalIF":7.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816791","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":"Analytic model for the propagation of a collisionless neutral beam","authors":"Lynton Appel","doi":"10.1016/j.cpc.2025.109610","DOIUrl":"10.1016/j.cpc.2025.109610","url":null,"abstract":"<div><div>This paper introduces an analytical model for the propagation of collisionless neutral particles in neutral beam injection (NBI) systems. The model incorporates a novel approach using composite Gaussian basis functions to represent non-Gaussian source distributions and extends to two-dimensional source configurations under orthogonal separability assumptions. The method efficiently computes particle velocity and spatial distributions along beam trajectories, accounting for truncation effects due to transmission losses. The model has been implemented as a computational module in the Minerva framework and interfaced with the ITER Integrated Modelling &amp; Analysis Suite (IMAS).</div><div>A case study of the MAST Upgrade NBI system demonstrates the model's ability to predict particle distributions from the source grid to the plasma cavity while accommodating detailed baffle geometries and calculating transmission factors. Comparisons reveal that reduced Gaussian basis representations can achieve an order-of-magnitude reduction in computational time with negligible impact on accuracy. The proposed model provides a fast and rigorous alternative to Monte Carlo simulations, enabling enhanced diagnostic modelling and efficient integration with Bayesian inference frameworks.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109610"},"PeriodicalIF":7.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830125","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":"Semi-automatic calculations of multi-loop Feynman amplitudes with AmpRed","authors":"Wen Chen","doi":"10.1016/j.cpc.2025.109607","DOIUrl":"10.1016/j.cpc.2025.109607","url":null,"abstract":"<div><div>We present a Mathematica package <strong>AmpRed</strong> for the semi-automatic calculations of multi-loop Feynman amplitudes with high efficiency and precision. <strong>AmpRed</strong> implements the methods of integration by parts and differential equations in the Feynman-parameter representation. It allows for the calculations of general parametric integrals (which may not have momentum-space correspondences). Various user-friendly tools for multi-loop calculations, such as those to construct and solve differential equations for Feynman integrals, are provided. It can also deal with tensor algebras in non-relativistic field theories. Interfaces to some packages, like <span>QGRAF</span> and FORM, are also provided.</div></div><div><h3>Program summary</h3><div><em>Program title:</em> <strong>AmpRed</strong>, version 1.0</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/swnf723tdh.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://gitlab.com/chenwenphy/ampred</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT license</div><div><em>Programming language:</em> Wolfram Mathematica 10.0, or newer</div><div><em>Nature of problem:</em> Reduce Feynman amplitudes to linear combinations of master integrals, and calculate master integrals numerically.</div><div><em>Solution method:</em> Reduce Feynman amplitudes by using the methods developed in refs. [1-3], and calculate master integrals recursively by using the method developed in ref. [4].</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>W. Chen, Reduction of Feynman integrals in the parametric representation, J. High Energy Phys. 02 (2020) 115.</div></span></li><li><span>[2]</span><span><div>W. Chen, Reduction of Feynman integrals in the parametric representation II: reduction of tensor integrals, Eur. Phys. J. C 81 (2021) 244.</div></span></li><li><span>[3]</span><span><div>W. Chen, Reduction of Feynman integrals in the parametric representation III: integrals with cuts, Eur. Phys. J. C 80 (2020) 1173.</div></span></li><li><span>[4]</span><span><div>W. Chen, M.-x. Luo, T.-Z. Yang, H.X. Zhu, Soft theorem to three loops in QCD and <span><math><mi>N</mi><mo>=</mo><mn>4</mn></math></span> super Yang-Mills theory, J. High Energy Phys. 01 (2024) 131.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109607"},"PeriodicalIF":7.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791928","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":"OPITeR: A program for tensor reduction of multi-loop Feynman integrals","authors":"Jae Goode,&nbsp;Franz Herzog,&nbsp;Sam Teale","doi":"10.1016/j.cpc.2025.109606","DOIUrl":"10.1016/j.cpc.2025.109606","url":null,"abstract":"<div><div>We present <span>OPITeR</span>, a <span>Form</span> program for the reduction of multi-loop tensor Feynman integrals. The program can handle tensors, including spinor indices, with rank of up to 20 and can deal with up to 8 independent external momenta. The reduction occurs in <em>D</em> dimensions compatible with conventional dimensional regularization. The program is able to manifest symmetries of the integrand in the tensor reduced form.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109606"},"PeriodicalIF":7.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808327","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":"TPC track denoising and recognition using convolutional neural networks","authors":"Matěj Gajdoš ,&nbsp;Hugo Natal da Luz ,&nbsp;Geovane G.A. Souza ,&nbsp;Marco Bregant","doi":"10.1016/j.cpc.2025.109608","DOIUrl":"10.1016/j.cpc.2025.109608","url":null,"abstract":"<div><div>The capability of convolutional neural networks to remove spurious signals caused by electronic noise, microdischarges and other effects from experimental data obtained with Time Projection Chambers is studied. A generator of synthetic data for the training of the neural network is described and its performance is compared with the results obtained with a conventional algorithm. The Physical meaning of the data resulting from the neural network and conventional denoising algorithms is thoroughly analysed, demonstrating the potential of convolutional neural networks in the preparation of raw data for analysis.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109608"},"PeriodicalIF":7.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791927","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":"Graspg – An extension to Grasp2018 based on configuration state function generators","authors":"Ran Si ,&nbsp;Yanting Li ,&nbsp;Kai Wang ,&nbsp;Chongyang Chen ,&nbsp;Gediminas Gaigalas ,&nbsp;Michel Godefroid ,&nbsp;Per Jönsson","doi":"10.1016/j.cpc.2025.109604","DOIUrl":"10.1016/j.cpc.2025.109604","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The &lt;span&gt;Graspg&lt;/span&gt; program package is an extension to &lt;span&gt;Grasp&lt;/span&gt;2018 (Froese Fischer et al. (2019) &lt;span&gt;&lt;span&gt;[1]&lt;/span&gt;&lt;/span&gt;) based on configuration state function generators (CSFGs). The generators keep spin-angular integrations at a minimum and reduce substantially the execution time and the memory requirement for large-scale multiconfiguration Dirac-Hartree-Fock (MCDHF) and relativistic configuration interaction (CI) atomic structure calculations. The package includes the improvements reported in Li (2023) &lt;span&gt;&lt;span&gt;[8]&lt;/span&gt;&lt;/span&gt; in terms of redesigned and efficient constructions of direct and exchange potentials and Lagrange multipliers. In addition, further parallelization of the diagonalization procedure has been implemented. Tools have been developed for predicting configuration state functions (CSFs) that are unimportant and can be discarded for large MCDHF or CI calculations based on results from smaller calculations, thus providing efficient methods for &lt;em&gt;a priori&lt;/em&gt; condensation. The package provides a seamless interoperability with &lt;span&gt;Grasp2018&lt;/span&gt;. From extensive test runs and benchmarking, we have demonstrated reductions in the execution time and disk file sizes with factors of 37 and 98, respectively, for MCDHF calculations based on large orbital sets compared to corresponding &lt;span&gt;Grasp2018&lt;/span&gt; calculations. For CI calculations, reductions of the execution time with factors over 200 have been attained. With a sensible use of the new possibilities for &lt;em&gt;a priori&lt;/em&gt; condensation, CI calculations with nominally hundreds of millions of CSFs can be handled.&lt;/div&gt;&lt;div&gt;&lt;strong&gt;PROGRAM SUMMARY&lt;/strong&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; &lt;span&gt;Graspg&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/7b5kbhy3v9.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; MIT License&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Fortran 95&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Prediction of atomic energy levels using a multiconfiguration Dirac–Hartree–Fock approach.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; The computational method is the same as in &lt;span&gt;Grasp2018&lt;/span&gt; [1] except that configuration state function generators (CSFGs) have been introduced, a concept that substantially reduces the execution times and memory requirements for large-scale calculations [2]. The method also relies on redesigned and more efficient constructions of direct and exchange potentials and Lagrange multipliers, along with additional parallelization of the diagonalization procedure as detailed in [3].&lt;/div&gt;&lt;div&gt;&lt;em&gt;Additional comments including restrictions and unusual features:&lt;/em&gt; 1. provides a seamless interoperability with &lt;span&gt;Grasp&lt;/span&gt;2018, 2. options to limit the Breit interaction, 3. includes tools for predicting CSFs that are unimportant and can be discarded for large MCDHF or CI calculations based on the results from smaller calculations","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109604"},"PeriodicalIF":7.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826475","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":"Discovery and inversion of the viscoelastic wave equation in inhomogeneous media","authors":"Su Chen ,&nbsp;Yi Ding ,&nbsp;Hiroe Miyake ,&nbsp;Xiaojun Li","doi":"10.1016/j.cpc.2025.109599","DOIUrl":"10.1016/j.cpc.2025.109599","url":null,"abstract":"<div><div>In scientific machine learning, the task of identifying partial differential equations accurately from sparse and noisy data poses a significant challenge. Current sparse regression methods may identify inaccurate equations on sparse and noisy datasets and are not suitable for varying coefficients. To address this issue, we propose a hybrid framework that combines two alternating direction optimization phases: discovery and embedding. The discovery phase employs current well-developed sparse regression techniques to preliminarily identify governing equations from observations. The embedding phase implements a recurrent convolutional neural network (RCNN), enabling efficient processes for time-space iterations involved in discretized forms of wave equation. The RCNN model further optimizes the imperfect sparse regression results to obtain more accurate functional terms and coefficients. Through alternating update of discovery-embedding phases, essential physical equations can be robustly identified from noisy and low-resolution measurements. To assess the performance of proposed framework, numerical experiments are conducted on various scenarios involving wave equation in elastic/viscoelastic and homogeneous/inhomogeneous media. The results demonstrate that the proposed method exhibits excellent robustness and accuracy, even when faced with high levels of noise and limited data availability in both spatial and temporal domains.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109599"},"PeriodicalIF":7.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791760","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":"Evaluation of the generalized Fermi-Dirac integral and its derivatives for moderate/large values of the parameters. New version announcement","authors":"Amparo Gil ,&nbsp;Andrzej Odrzywołek ,&nbsp;Javier Segura ,&nbsp;Nico M. Temme","doi":"10.1016/j.cpc.2025.109605","DOIUrl":"10.1016/j.cpc.2025.109605","url":null,"abstract":"<div><div>A revised version of the Matlab implementations of the expansions for the Fermi-Dirac integral and its derivatives is presented. In the new version, our functions for computing the Kummer functions <span><math><mi>M</mi><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>,</mo><mi>x</mi><mo>)</mo></math></span> and <span><math><mi>U</mi><mo>(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo>,</mo><mi>x</mi><mo>)</mo></math></span> are incorporated into the software. The algorithms for computing the Kummer functions are described in [1,2]. In this way, the implementations of the expansions for the Fermi-Dirac integral can be used in earlier Matlab versions and can be easily adapted to GNU Octave. The efficiency of the computations is also greatly improved.</div></div><div><h3>New version program summary</h3><div><em>Program Title:</em> FermiDiracExpans</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/sk34wtcxhh.2</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> Matlab</div><div><em>Journal reference of previous version:</em> Comput. Phys. Commun. 283 (2023) 108563</div><div><em>Does the new version supersede the previous version?:</em> Yes</div><div><em>Reasons for the new version:</em> With the new version, the implementations of the expansions for the Fermi-Dirac integral can be used in earlier Matlab versions and can be easily adapted to GNU Octave. The efficiency of the computations is also greatly improved.</div><div><em>Summary of revisions:</em> The built-in Matlab functions <span>kummerU</span> and <span>hypergeom</span> are replaced by our functions <span>Uabx</span> and <span>Mabx</span>, respectively. These functions improve both the accuracy and efficiency of the built-in Matlab functions for computing the Kummer functions. A few relations satisfied by the Kummer functions are used to adapt the expressions in the expansions involving Kummer functions with negative parameters into expressions with real positive parameters and arguments, as used in our algorithms for Kummer functions.</div><div><em>Nature of problem:</em> The evaluation of the relativistic Fermi-Dirac function and its partial derivatives is needed in different problems in applied and theoretical physics, such as stellar astrophysics, plasma physics or electronics.</div><div><em>Solution method:</em> Convergent and asymptotic expansions are provided to approximate the relativistic Fermi-Dirac function and its derivatives for moderate/large values of its parameters.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>A. Gil, D. Ruiz-Antolin, J. Segura, N.M. Temme, Numer. Algorithms 94 (2023) 669–679.</div></span></li><li><span>[2]</span><span><div>A. Gil, D. Ruiz-Antolin, J. Segura, N.M. Temme, Lecture Notes in Computer Science, vol. 14477, Springer, Cham, 2025.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109605"},"PeriodicalIF":7.2,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760109","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":"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}