Computer Physics Communications最新文献

{"title":"curvedSpaceSim: A framework for simulating particles interacting along geodesics","authors":"Toler H. Webb,&nbsp;Daniel M. Sussman","doi":"10.1016/j.cpc.2025.109545","DOIUrl":"10.1016/j.cpc.2025.109545","url":null,"abstract":"<div><div>A large number of powerful, high-quality, and open-source simulation packages exist to efficiently perform molecular dynamics simulations, and their prevalence has greatly accelerated discoveries across a wide range of scientific domains. These packages typically simulate particles in flat (Euclidean) space, with options to specify a variety of boundary conditions. While more exotic, many physical systems are constrained to and interact across curved surfaces, such as organisms moving across the landscape, colloids pinned at curved fluid-fluid interfaces, and layers of epithelial cells forming highly curved tissues. The calculation of distances and the updating of equations of motion in idealized geometries (namely, on surfaces of constant curvature) can be done analytically, but it is much more challenging to efficiently perform molecular-dynamics-like simulations on arbitrarily curved surfaces. This article discusses a simulation framework which combines tools from particle-based simulations with recent work in discrete differential geometry to model particles that interact via geodesic distances and move on an arbitrarily curved surface. We present computational cost estimates for a variety of surface complexities with and without various algorithmic specializations (e.g., restrictions to short-range interaction potentials, or multi-threaded parallelization). Our flexible and extensible framework is set up to easily handle both equilibrium and non-equilibrium dynamics, and will enable researchers to access time- and particle-number-scales previously inaccessible.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> curvedSpaceSim</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/wc7nxf93ym.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/sussmanLab/curvedSpaceSim</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> C<strong>++</strong></div><div><em>Nature of problem:</em> Molecular-dynamics-like simulations of degrees of freedom evolving on a curved two-dimensional manifold according to standard equilibrium or non-equilibrium equations of motion and interacting via geodesics.</div><div><em>Solution method:</em> We discretize both time and space, using modern tools from discrete differential geometry to efficiently find geodesic paths and distances. MPI parallelization is implemented to access large system sizes, and where appropriate (e.g., when dealing with short-ranged inter-particle potentials) we implement the ability to aggressively prune data structures, greatly decreasing the computational cost of our many-particle simulations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109545"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428087","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":"Deep learning-enhanced reduced-order ensemble Kalman filter for efficient Bayesian data assimilation of parametric PDEs","authors":"Yanyan Wang ,&nbsp;Liang Yan ,&nbsp;Tao Zhou","doi":"10.1016/j.cpc.2025.109544","DOIUrl":"10.1016/j.cpc.2025.109544","url":null,"abstract":"<div><div>Bayesian data assimilation for systems governed by parametric partial differential equations (PDEs) is computationally demanding due to the need for multiple forward model evaluations. Reduced-order models (ROMs) have been widely used to reduce the computational burden. However, traditional ROM techniques rely on linear mode superposition, which frequently fails to capture nonlinear time-dependent dynamics efficiently and leads to biases in the assimilation results. To address these limitations, we introduce a new deep learning-enhanced reduced-order ensemble Kalman filter (DR-EnKF) method for Bayesian data assimilation. The proposed approach employs a two-tiered learning framework. First, the full-order model is reduced using operator inference, which finds the primary dynamics of the system through long-term simulations generated from coarse-grid data. Second, a model error network is trained with short-term simulation data from a fine grid to learn about the ROM-induced discrepancy. The learned network is then used online to correct the ROM-based EnKF, resulting in more accurate state updates during the assimilation process. The performance of the proposed method is evaluated on several benchmark problems, including the Burgers' equation, the FitzHugh-Nagumo model, and advection-diffusion-reaction systems. The results show considerable computational speedup without compromising accuracy, making this approach an effective tool for large-scale data assimilation tasks.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109544"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464046","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":"JAX-based aeroelastic simulation engine for differentiable aircraft dynamics","authors":"Alvaro Cea,&nbsp;Rafael Palacios","doi":"10.1016/j.cpc.2025.109547","DOIUrl":"10.1016/j.cpc.2025.109547","url":null,"abstract":"&lt;div&gt;&lt;div&gt;A novel methodology is presented in this paper for the structural and aeroelastic analysis of large flexible systems with slender, streamlined components, such as aircraft or wind turbines. Leveraging on the numerical library JAX, a nonlinear formulation based on velocities and strains enables a highly vectorised codebase that is especially suitable for the integration of aerodynamic loads which naturally appear as follower forces. In addition to that, JAX automatic differentiation capabilities are used to obtain gradients that allow the solver to be embedded into broader multidisciplinary optimization frameworks. The general solution starts from a linear Finite-Element (FE) model of arbitrary complexity, on which a structural model order reduction is performed. A nonlinear description of the reduced model follows, with the corresponding reconstruction of the full 3D dynamics. It is shown to be highly accurate and efficient on representative aircraft models are shown. An extensive verification has been carried out by comparison with MSC Nastran full-FE linear and nonlinear solutions. Furthermore the nonlinear gust response of a full aircraft configuration with over half a million degrees-of-freedom is computed, and it is faster than its frequency-based, linear equivalent as implemented by a commercial package. Therefore this could be harnessed by aircraft loads engineers to add geometrically nonlinear effects to their existing workflows at no extra computational effort. Finally, automatic differentiation on both static and dynamic problems is validated against finite-differences, which combined with a near real-time performance of the solvers opens new possibilities for aeroelastic studies and design optimization.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; FENIAX&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/wxy56w8j6y.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;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/ACea15/FENIAX&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;https://github.com/ACea15/FENIAX/tree/master/docs/reports/CPC24&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; GNU GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Python&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Aeroelastic solutions that couple structural and fluid domains are paramount in the study of many engineering structures such aeroplanes, bridges or wind-turbines. They often feature slender and light components that can potentially undergo large deflections that require of geometrically nonlinear modelling tools, which are linked to higher computational resources and potentially prohibitively simulation times. Moreover, since the advent of computers, organizations have gathered an expertise to build large finite-element-based aeroelastic models based on linear formulations that might not be easily amendable for nonlinear analysis. We pr","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109547"},"PeriodicalIF":7.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437728","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":"CaLES: A GPU-accelerated solver for large-eddy simulation of wall-bounded flows","authors":"Maochao Xiao ,&nbsp;Alessandro Ceci ,&nbsp;Pedro Costa ,&nbsp;Johan Larsson ,&nbsp;Sergio Pirozzoli","doi":"10.1016/j.cpc.2025.109546","DOIUrl":"10.1016/j.cpc.2025.109546","url":null,"abstract":"<div><div>We introduce CaLES, a GPU-accelerated finite-difference solver designed for large-eddy simulations (LES) of incompressible wall-bounded flows in massively parallel environments. Built upon the existing direct numerical simulation (DNS) solver CaNS, CaLES relies on low-storage, third-order Runge-Kutta schemes for temporal discretization, with the option to treat viscous terms via an implicit Crank-Nicolson scheme in one or three directions. A fast direct solver, based on eigenfunction expansions, is used to solve the discretized Poisson/Helmholtz equations. For turbulence modeling, the classical Smagorinsky model with van Driest near-wall damping and the dynamic Smagorinsky model are implemented, along with a logarithmic law wall model. GPU acceleration is achieved through OpenACC directives, following CaNS-2.3.0. Performance assessments were conducted on the Leonardo cluster at CINECA, Italy. Each node is equipped with one Intel Xeon Platinum 8358 CPU (2.60 GHz, 32 cores) and four NVIDIA A100 GPUs (64 GB HBM2e), interconnected via NVLink 3.0 (200 GB/s). The inter-node communication bandwidth is 25 GB/s, supported by a DragonFly+ network architecture with NVIDIA Mellanox InfiniBand HDR. Results indicate that the computational speed on a single GPU is equivalent to approximately 15 CPU nodes, depending on the treatment of viscous terms and the subgrid-scale model, and that the solver efficiently scales across multiple GPUs. The predictive capability of CaLES has been tested using multiple flow cases, including decaying isotropic turbulence, turbulent channel flow, and turbulent duct flow. The high computational efficiency of the solver enables grid convergence studies on extremely fine grids, pinpointing non-monotonic grid convergence for wall-modeled LES.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109546"},"PeriodicalIF":7.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427574","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":"Enhancing precision in J/ψ mass estimation: A study of ensemble and deep learning methods","authors":"Serpil Yalcin Kuzu ,&nbsp;Ayben Karasu Uysal ,&nbsp;Mustafa Kaya","doi":"10.1016/j.cpc.2025.109534","DOIUrl":"10.1016/j.cpc.2025.109534","url":null,"abstract":"<div><div>This study evaluates ensemble learning methods and Deep Neural Networks (DNNs) for identifying <em>J/</em><span><math><mi>ψ</mi><mo>→</mo><msup><mrow><mi>μ</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>μ</mi></mrow><mrow><mo>−</mo></mrow></msup></math></span> events in proton-proton collisions at the LHC, focusing on the dimuon decay channel within a skewed dataset. For this purpose, 8 different machine learning models based on Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and DNNs were implemented to investigate the most effective approach for charmonium event determination. Performance metrics such as precision, recall, F-1 Score, geometric mean (G-mean), and balanced accuracy (BAcc) are employed, with StratifiedKFold cross-validation verifying the models' robustness in skewed data scenarios. Results demonstrate DNNs as the most proficient, underscoring their potential in complex data analysis in particle physics. Utilizing the Crystal Ball (CB) function on the results of DNNs, the precision of the <em>J/ψ</em> mass was estimated. This study not only enhances understanding of machine learning applications in high-energy particle collisions but also sets the stage for more advanced research in this field.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109534"},"PeriodicalIF":7.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419691","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":"Modeling Cosmic-Ray Transport: A CRPropa based stochastic differential equation solver","authors":"Lukas Merten ,&nbsp;Sophie Aerdker","doi":"10.1016/j.cpc.2025.109542","DOIUrl":"10.1016/j.cpc.2025.109542","url":null,"abstract":"<div><div>We present a new code that significantly extends CRPropa's capabilities to model the ensemble averaged transport of charged cosmic rays in turbulent magnetic fields. Compared with previous implementations, the new version allows for spatially varying Eigenvalues of the diffusion tensor and for the implementation of drifts associated with the magnetic background field. The software is based on solving a set of stochastic differential equations (SDEs).</div><div>In this work we give detailed instructions to transform a transport equation, usually given as a partial differential equation, into a Fokker-Planck equation and further into the corresponding set of SDEs. Furthermore, detailed tests of the algorithms are done and different sources of uncertainties are compared to each other. So to some extent, this work serves as a technical reference for existing and upcoming work using the new generalized SDE solver based on the CRPropa framework.</div><div>Furthermore, the new flexibility allowed us to implement first test cases on continuous particle injection and focused pitch angle diffusion.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109542"},"PeriodicalIF":7.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427534","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":"A posteriori analysis and adaptive algorithms for blended type atomistic-to-continuum coupling with higher-order finite elements","authors":"Yangshuai Wang","doi":"10.1016/j.cpc.2025.109533","DOIUrl":"10.1016/j.cpc.2025.109533","url":null,"abstract":"<div><div>The accurate and efficient simulation of material systems with defects using atomistic-to-continuum (a/c) coupling methods is a significant focus in computational materials science. Achieving a balance between accuracy and computational cost requires the application of <em>a posteriori</em> error analysis and adaptive algorithms. In this paper, we provide a rigorous <em>a posteriori</em> error analysis for three common blended a/c methods: the blended energy-based quasi-continuum (BQCE) method, the blended force-based quasi-continuum (BQCF) method, and the atomistic/continuum blending with ghost force correction (BGFC) method. We discretize the Cauchy-Born model in the continuum region using first- and second-order finite element methods, with the potential for extending to higher-order schemes. The resulting error estimator provides both an upper bound on the true error and a reliable lower bound, subject to a controllable truncation term. Furthermore, we offer an a posteriori analysis of the energy error. We develop and implement an adaptive mesh refinement algorithm applied to two typical defect scenarios: a micro-crack and a Frenkel defect. In both cases, our numerical experiments demonstrate optimal convergence rates with respect to degrees of freedom, in agreement with a priori error estimates.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109533"},"PeriodicalIF":7.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419692","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":"GPU-native adaptive mesh refinement with application to lattice Boltzmann simulations","authors":"Khodr Jaber ,&nbsp;Ebenezer E. Essel ,&nbsp;Pierre E. Sullivan","doi":"10.1016/j.cpc.2025.109543","DOIUrl":"10.1016/j.cpc.2025.109543","url":null,"abstract":"<div><div>Adaptive Mesh Refinement (AMR) enables efficient computation of flows by providing high resolution in critical regions while allowing for coarsening in areas where fine detail is unnecessary. While early AMR software packages relied solely on CPU parallelization, the widespread adoption of heterogeneous computing systems has led to GPU-accelerated implementations. In these hybrid approaches, simulation data typically resides on the GPU, and mesh management and adaptation occur exclusively on the CPU, necessitating frequent data transfers between them. A more efficient strategy is to adapt and maintain the entire mesh structure exclusively on the GPU, eliminating these transfers. Because of its inherent parallelism, the Lattice Boltzmann Method (LBM) has been widely implemented in hybrid AMR frameworks. This work presents a GPU-native algorithm for AMR using a block-based forest of octrees approach, implemented in both two and three dimensions as open-source C++/CUDA code. The implementation includes a Lattice Boltzmann solver for weakly compressible flow, though the underlying grid refinement procedure is compatible with any solver operating on cell-centered block-based grids. The lid-driven cavity and flow past a square cylinder benchmarks validate the algorithm's effectiveness across multiple velocity sets in both single- and double-precision. Tests conducted on consumer and datacenter-grade GPUs demonstrate its versatility across different hardware platforms.</div><div>Link to repository: <span><span>https://github.com/KhodrJ/AGAL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109543"},"PeriodicalIF":7.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463914","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":"ms2: A molecular simulation tool for thermodynamic properties, release 5.0","authors":"Isabel Nitzke ,&nbsp;Gabriela Guevara-Carrion ,&nbsp;Denis Saric ,&nbsp;Simon Homes ,&nbsp;Simon Stephan ,&nbsp;Robin Fingerhut ,&nbsp;Martin Bernreuther ,&nbsp;Hans Hasse ,&nbsp;Jadran Vrabec","doi":"10.1016/j.cpc.2025.109541","DOIUrl":"10.1016/j.cpc.2025.109541","url":null,"abstract":"<div><div>A new version release (5.0) of the molecular simulation tool <span><math><mi>m</mi><mi>s</mi><mn>2</mn></math></span> (Deublein et al. 2011; Glass et al. 2014; Rutkai et al. 2017; Fingerhut et al. 2021) is presented. Version 5.0 of <span><math><mi>m</mi><mi>s</mi><mn>2</mn></math></span> features the eight statistical ensembles that are accessible via Monte Carlo simulation for pure fluids and mixtures. It introduces the Lustig formalism for all ensembles which allows on-the-fly sampling of any time-independent thermodynamic property, such as isochoric and isobaric heat capacities, thermal expansion coefficient, isothermal compressibility, thermal pressure coefficient, speed of sound or Joule-Thomson coefficient. Through the introduction of an extended Axilrod-Teller-Muto potential, three-body interactions become available, also incorporating an improved parallelization scheme. In combination with an extension of the Tang-Toennies potential, this provides a highly accurate intermolecular potential for krypton. Moreover, a truncated and shifted Mie potential for arbitrary cutoff radii is implemented, transport property calculations are extended and an auxiliary tool for the determination of Brown's characteristic curves is introduced.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109541"},"PeriodicalIF":7.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419690","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":"A GPU-accelerated numerical model for nearshore scalar transport by dispersive shallow water flows","authors":"Sooncheol Hwang ,&nbsp;Patrick J. Lynett ,&nbsp;Sangyoung Son","doi":"10.1016/j.cpc.2025.109539","DOIUrl":"10.1016/j.cpc.2025.109539","url":null,"abstract":"&lt;div&gt;&lt;div&gt;A GPU-accelerated nearshore scalar transport model with the Boussinesq-type wave solver is introduced. The depth-integrated advection-diffusion equation is implemented into Celeris Advent, the firstly-developed open-source Boussinesq wave model equipped with an interactive system supporting simultaneous visualization and data exchange between a user and the computing unit. A hybrid finite volume-finite difference scheme is adopted to discretize the governing equations, and the modified HLL Riemann solver for satisfying the conservation property of the scalar concentration is applied for an accurate approximation of scalar numerical flux. A source-function wavemaker in conjunction with alongshore periodic boundary conditions and a wave-breaking model are implemented to more precisely replicate the nearshore hydrodynamic processes. Several numerical tests using analytical solutions and experimental data are performed to validate the model. Finally, field-scale dye release experiments are reproduced numerically, assessing the applicability of the proposed model in predicting nearshore scalar transport by dispersive hydrodynamics. The proposed model is expected to serve as an advanced tool for real-time assessment and mitigation of marine pollution incidents.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; Celeris-with-scalar-transport&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/bk7v57wsxj.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;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.5281/zenodo.10609197&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; GNU General Public License 3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; C++, HLSL&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; Movies 1-4&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Nearshore scalar transport phenomena have generally been investigated through the numerical models that solve the shallow water equations and the advection-diffusion equation due to their high computational efficiency. However, these models are incapable of simulating the dispersive effects of the waves, which are significant in nearshore hydrodynamics. The scalar transport model with a Boussinesq-type solver can precisely approximate the nearshore scalar transport processes, but its application has been limited by the heavy computational load, which hinders real-time simulations. Building on previous work (Celeris Advent), this software enables real-time numerical simulation of nearshore scalar transport as well as simultaneous visualization. It also supports an interactive environment, allowing the user to change the water surface, bathymetry, and scalar concentration while the model is running.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; A hybrid finite volume-finite difference scheme is used to solve the extended Boussinesq equations and the advection-diffusion equation. Various components, including the modi","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109539"},"PeriodicalIF":7.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387758","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}