Pub Date : 2025-02-07DOI: 10.1016/j.cpc.2025.109540
Bob Zigon , Luoding Zhu
We introduce MoE-Bolt (Mixture of Experts for lattice Boltzman), a novel neural network approach for predicting the unsteady state of fluid flow past a cylinder. We modeled the problem as a sequence prediction where 8 time steps previous to time t were used to predict the velocity fields of time t. With Reynolds numbers in the training set from 138 to 196, the problem was difficult because the flow was in an unsteady-state. We used a mixture of experts (MoE) to work cooperatively on solving the problem. The advantage of this cooperation is that the computing domain was decomposed without human intervention. When 4 experts were used our solution exhibited a 15 decibel improvement in the signal to noise ratio when compared to the single expert configuration. Our results and analyses show that MoE-Bolt is an effective approach for unsteady flows and it is a stepping stone for predicting flow fields at all time instants without using data from the simulation.
{"title":"Modeling 2D unsteady flows at moderate Reynolds numbers using a 3D convolutional neural network and a mixture of experts","authors":"Bob Zigon , Luoding Zhu","doi":"10.1016/j.cpc.2025.109540","DOIUrl":"10.1016/j.cpc.2025.109540","url":null,"abstract":"<div><div>We introduce MoE-Bolt (Mixture of Experts for lattice Boltzman), a novel neural network approach for predicting the unsteady state of fluid flow past a cylinder. We modeled the problem as a sequence prediction where 8 time steps previous to time <em>t</em> were used to predict the velocity fields of time <em>t</em>. With Reynolds numbers in the training set from 138 to 196, the problem was difficult because the flow was in an unsteady-state. We used a mixture of experts (MoE) to work cooperatively on solving the problem. The advantage of this cooperation is that the computing domain was decomposed without human intervention. When 4 experts were used our solution exhibited a 15 decibel improvement in the signal to noise ratio when compared to the single expert configuration. Our results and analyses show that MoE-Bolt is an effective approach for unsteady flows and it is a stepping stone for predicting flow fields at all time instants without using data from the simulation.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109540"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379241","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}
Pub Date : 2025-02-07DOI: 10.1016/j.cpc.2025.109538
Xin Guan , Xiao Liu , Yan-Qing Ma , Wen-Hao Wu
In this article, we present the package Blade as the first implementation of the block-triangular form improved Feynman integral reduction method. The block-triangular form has orders of magnitude fewer equations compared to the plain integration-by-parts system, allowing for strictly block-by-block solutions. This results in faster evaluations and reduced resource consumption. We elucidate the algorithms involved in obtaining the block-triangular form along with their implementations. Additionally, we introduce novel algorithms for finding the canonical form and symmetry relations of Feynman integrals, as well as for performing spanning-sector reduction. Our benchmarks for various state-of-the-art problems demonstrate that Blade is remarkably competitive among existing reduction tools. Furthermore, the Blade package offers several distinctive features, including support for complex kinematic variables or masses, user-defined Feynman prescriptions for each propagator, and general integrands.
Program summary
Program Title:Blade
CPC Library link to program files:https://doi.org/10.17632/rzfwjzmd26.1
https://github.com/peraro/finiteflow, open source.
{"title":"Blade: A package for block-triangular form improved Feynman integrals decomposition","authors":"Xin Guan , Xiao Liu , Yan-Qing Ma , Wen-Hao Wu","doi":"10.1016/j.cpc.2025.109538","DOIUrl":"10.1016/j.cpc.2025.109538","url":null,"abstract":"<div><div>In this article, we present the package <span>Blade</span> as the first implementation of the block-triangular form improved Feynman integral reduction method. The block-triangular form has orders of magnitude fewer equations compared to the plain integration-by-parts system, allowing for strictly block-by-block solutions. This results in faster evaluations and reduced resource consumption. We elucidate the algorithms involved in obtaining the block-triangular form along with their implementations. Additionally, we introduce novel algorithms for finding the canonical form and symmetry relations of Feynman integrals, as well as for performing spanning-sector reduction. Our benchmarks for various state-of-the-art problems demonstrate that <span>Blade</span> is remarkably competitive among existing reduction tools. Furthermore, the <span>Blade</span> package offers several distinctive features, including support for complex kinematic variables or masses, user-defined Feynman prescriptions for each propagator, and general integrands.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>Blade</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/rzfwjzmd26.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://gitee.com/multiloop-pku/blade</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT</div><div><em>Programming language:</em> <span>Wolfram Mathematica</span> 11.3 or higher</div><div><em>External routines/libraries used:</em> <span>Wolfram Mathematica</span> [1], <span>FiniteFlow</span> [2]</div><div><em>Nature of problem:</em> Automatically reducing dimensionally regularized Feynman integrals into linear combination of master integrals.</div><div><em>Solution method:</em> The program implements recently proposed block-triangular form to significantly improve the reduction efficiency.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div><span><span>http://www.wolfram.com/mathematica</span><svg><path></path></svg></span>, commercial algebraic software.</div></span></li><li><span>[2]</span><span><div><span><span>https://github.com/peraro/finiteflow</span><svg><path></path></svg></span>, open source.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109538"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394345","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}
Pub Date : 2025-02-07DOI: 10.1016/j.cpc.2025.109536
Dean Muir , Kenneth Duru , Matthew Hole , Stuart Hudson
We present a novel numerical method for solving the anisotropic diffusion equation in magnetic fields confined to a periodic box which is accurate and provably stable. We derive energy estimates of the solution of the continuous initial boundary value problem. A discrete formulation is presented using operator splitting in time with the summation by parts finite difference approximation of spatial derivatives for the perpendicular diffusion operator. Weak penalty procedures are derived for implementing both boundary conditions and parallel diffusion operator obtained by field line tracing. We prove that the fully-discrete approximation is unconditionally stable. Discrete energy estimates are shown to match the continuous energy estimate given the correct choice of penalty parameters. A nonlinear penalty parameter is shown to provide an effective method for tuning the parallel diffusion penalty and significantly minimises rounding errors. Several numerical experiments, using manufactured solutions, the “NIMROD benchmark” problem and a single island problem, are presented to verify numerical accuracy, convergence, and asymptotic preserving properties of the method. Finally, we present a magnetic field with chaotic regions and islands and show the contours of the anisotropic diffusion equation reproduce key features in the field.
{"title":"A provably stable numerical method for the anisotropic diffusion equation in confined magnetic fields","authors":"Dean Muir , Kenneth Duru , Matthew Hole , Stuart Hudson","doi":"10.1016/j.cpc.2025.109536","DOIUrl":"10.1016/j.cpc.2025.109536","url":null,"abstract":"<div><div>We present a novel numerical method for solving the anisotropic diffusion equation in magnetic fields confined to a periodic box which is accurate and provably stable. We derive energy estimates of the solution of the continuous initial boundary value problem. A discrete formulation is presented using operator splitting in time with the summation by parts finite difference approximation of spatial derivatives for the perpendicular diffusion operator. Weak penalty procedures are derived for implementing both boundary conditions and parallel diffusion operator obtained by field line tracing. We prove that the fully-discrete approximation is unconditionally stable. Discrete energy estimates are shown to match the continuous energy estimate given the correct choice of penalty parameters. A nonlinear penalty parameter is shown to provide an effective method for tuning the parallel diffusion penalty and significantly minimises rounding errors. Several numerical experiments, using manufactured solutions, the “NIMROD benchmark” problem and a single island problem, are presented to verify numerical accuracy, convergence, and asymptotic preserving properties of the method. Finally, we present a magnetic field with chaotic regions and islands and show the contours of the anisotropic diffusion equation reproduce key features in the field.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109536"},"PeriodicalIF":7.2,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379245","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}
Pub Date : 2025-02-06DOI: 10.1016/j.cpc.2025.109529
Song-En Chen , Eugene Su , Chih-Chung Wang , Jia-Han Li , Chao-Ching Ho
In this paper, we propose a singular value decomposition-based deep learning model to investigate the inverse problem between simulated near field electromagnetic data and the geometric parameters of through silicon via array. This is of great importance for predicting the critical dimensions of through silicon via in the semiconductor industry, and it becomes more challenging due to the decreasing size of through silicon via. Simulation of electromagnetic field data for various through silicon via arrays is used by the finite-difference time-domain method. We analyze the near-field electromagnetic intensity distribution of different geometric parameters, including critical dimensions such as depth, top diameter, bottom diameter, sidewall roughness, and bottom ellipsoid radius. Due to the sub-micron scale of the critical dimensions and the high aspect ratios, single-wavelength electric field data is insufficient for accurate predictions. However, due to its size, multi-wavelength electric field data presents a significant computational challenge. We employ singular value decomposition to compress the multi-wavelength electric field data to overcome this. By analyzing the dominant singular value components, we reduce the data volume to 4.56 % of its original size while preserving predictive accuracy. The compressed data is subsequently integrated with deep learning models for critical dimension prediction. We compare three model architectures and demonstrate that utilizing the largest singular values from 30-wavelength electric field data substantially improves the prediction of vertical critical dimensions, such as through silicon via depth and bottom ellipsoid depth. Specifically, the singular value decomposition-based deep learning model, which incorporates the largest singular values from 5-wavelength electric field data, reduces computation time by 34.88 % and decreases the mean absolute percentage error for through silicon via depth and bottom ellipsoid depth by 2.78 % and 6.60 %, respectively. The singular value decomposition based deep learning model, which uses the largest singular values from 30-wavelength data, further reduces the mean absolute percentage error for the depth and bottom ellipsoid depth of through silicon via by 2.86 % and 10.60 %. These findings underscore the efficacy of singular value decomposition-based multi-wavelength electric field data compression combined with deep learning, offering an efficient approach for managing large-scale electromagnetic simulations in through silicon via design. Our source code is available at https://github.com/AOI-Laboratory/EMDataSVD.
{"title":"Singular value decomposition of near-field electromagnetic data for compressing and accelerating deep neural networks in the prediction of geometric parameters for through silicon via array","authors":"Song-En Chen , Eugene Su , Chih-Chung Wang , Jia-Han Li , Chao-Ching Ho","doi":"10.1016/j.cpc.2025.109529","DOIUrl":"10.1016/j.cpc.2025.109529","url":null,"abstract":"<div><div>In this paper, we propose a singular value decomposition-based deep learning model to investigate the inverse problem between simulated near field electromagnetic data and the geometric parameters of through silicon via array. This is of great importance for predicting the critical dimensions of through silicon via in the semiconductor industry, and it becomes more challenging due to the decreasing size of through silicon via. Simulation of electromagnetic field data for various through silicon via arrays is used by the finite-difference time-domain method. We analyze the near-field electromagnetic intensity distribution of different geometric parameters, including critical dimensions such as depth, top diameter, bottom diameter, sidewall roughness, and bottom ellipsoid radius. Due to the sub-micron scale of the critical dimensions and the high aspect ratios, single-wavelength electric field data is insufficient for accurate predictions. However, due to its size, multi-wavelength electric field data presents a significant computational challenge. We employ singular value decomposition to compress the multi-wavelength electric field data to overcome this. By analyzing the dominant singular value components, we reduce the data volume to 4.56 % of its original size while preserving predictive accuracy. The compressed data is subsequently integrated with deep learning models for critical dimension prediction. We compare three model architectures and demonstrate that utilizing the largest singular values from 30-wavelength electric field data substantially improves the prediction of vertical critical dimensions, such as through silicon via depth and bottom ellipsoid depth. Specifically, the singular value decomposition-based deep learning model, which incorporates the largest singular values from 5-wavelength electric field data, reduces computation time by 34.88 % and decreases the mean absolute percentage error for through silicon via depth and bottom ellipsoid depth by 2.78 % and 6.60 %, respectively. The singular value decomposition based deep learning model, which uses the largest singular values from 30-wavelength data, further reduces the mean absolute percentage error for the depth and bottom ellipsoid depth of through silicon via by 2.86 % and 10.60 %. These findings underscore the efficacy of singular value decomposition-based multi-wavelength electric field data compression combined with deep learning, offering an efficient approach for managing large-scale electromagnetic simulations in through silicon via design. Our source code is available at <span><span>https://github.com/AOI-Laboratory/EMDataSVD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109529"},"PeriodicalIF":7.2,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403372","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}
Pub Date : 2025-02-05DOI: 10.1016/j.cpc.2025.109532
N. Vasileiadis , G. Tatsios , C. White , D.A. Lockerby , M.K. Borg , L. Gibelli
This paper presents uniGasFoam, an open-source particle-based solver for multiscale rarefied gas flow simulations, which has been developed within the well-established OpenFOAM framework, and is an extension of the direct simulation Monte Carlo (DSMC) solver dsmcFoam+. The developed solver addresses the coupling challenges inherent in hybrid continuum-particle methods, originating from the disparate nature of finite-volume (FV) solvers found in computational fluid dynamics (CFD) software and DSMC particle solvers. This is achieved by employing alternative stochastic particle methods, resembling DSMC, to tackle the continuum limit. The uniGasFoam particle-particle coupling produces a numerical implementation that is simpler and more robust, faster in many steady-state flows, and more scalable for transient flows compared to conventional continuum-particle coupling. The presented framework is unified and generic, and can couple DSMC with stochastic particle (SP) and unified stochastic particle (USP) methods, or be employed for pure DSMC, SP, and USP gas simulations. To enhance user experience, reduce required computational resources and minimise user error, advanced adaptive algorithms such as transient adaptive sub-cells, non-uniform cell weighting, and adaptive global time stepping have been integrated into uniGasFoam. In this paper, the hybrid USP-DSMC module of uniGasFoam is rigorously validated through multiple benchmark cases, consistently showing excellent agreement with pure DSMC, hybrid CFD-DSMC, and literature results. Notably, uniGasFoam achieves significant computational gains compared to pure dsmcFoam+ simulations, rendering it a robust computational tool well-suited for addressing multiscale rarefied gas flows of engineering importance.
Program summary
Program Title: uniGasFoam
CPC Library link to program files:https://doi.org/10.17632/9rvyjbvjw3.1
Licensing provisions: GNU General Public License 3
Programming language: C++
Nature of problem: uniGasFoam has been developed as an open-source framework for particle-based multiscale rarefied gas flow simulations.
Solution method: uniGasFoam implements an explicit time stepping solver with a hybrid stochastic molecular collision-relaxation scheme appropriate for studying multiscale rarefied gas flows.
{"title":"uniGasFoam: A particle-based OpenFOAM solver for multiscale rarefied gas flows","authors":"N. Vasileiadis , G. Tatsios , C. White , D.A. Lockerby , M.K. Borg , L. Gibelli","doi":"10.1016/j.cpc.2025.109532","DOIUrl":"10.1016/j.cpc.2025.109532","url":null,"abstract":"<div><div>This paper presents uniGasFoam, an open-source particle-based solver for multiscale rarefied gas flow simulations, which has been developed within the well-established OpenFOAM framework, and is an extension of the direct simulation Monte Carlo (DSMC) solver dsmcFoam+. The developed solver addresses the coupling challenges inherent in hybrid continuum-particle methods, originating from the disparate nature of finite-volume (FV) solvers found in computational fluid dynamics (CFD) software and DSMC particle solvers. This is achieved by employing alternative stochastic particle methods, resembling DSMC, to tackle the continuum limit. The uniGasFoam particle-particle coupling produces a numerical implementation that is simpler and more robust, faster in many steady-state flows, and more scalable for transient flows compared to conventional continuum-particle coupling. The presented framework is unified and generic, and can couple DSMC with stochastic particle (SP) and unified stochastic particle (USP) methods, or be employed for pure DSMC, SP, and USP gas simulations. To enhance user experience, reduce required computational resources and minimise user error, advanced adaptive algorithms such as transient adaptive sub-cells, non-uniform cell weighting, and adaptive global time stepping have been integrated into uniGasFoam. In this paper, the hybrid USP-DSMC module of uniGasFoam is rigorously validated through multiple benchmark cases, consistently showing excellent agreement with pure DSMC, hybrid CFD-DSMC, and literature results. Notably, uniGasFoam achieves significant computational gains compared to pure dsmcFoam+ simulations, rendering it a robust computational tool well-suited for addressing multiscale rarefied gas flows of engineering importance.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> uniGasFoam</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/9rvyjbvjw3.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/NVasileiadis93/uniGasFoam</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License 3</div><div><em>Programming language:</em> C++</div><div><em>Nature of problem:</em> uniGasFoam has been developed as an open-source framework for particle-based multiscale rarefied gas flow simulations.</div><div><em>Solution method:</em> uniGasFoam implements an explicit time stepping solver with a hybrid stochastic molecular collision-relaxation scheme appropriate for studying multiscale rarefied gas flows.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109532"},"PeriodicalIF":7.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143200071","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}
Pub Date : 2025-02-04DOI: 10.1016/j.cpc.2025.109523
Kejie Fu , Mingjie Liao , Yangshuai Wang , Jianjun Chen , Lei Zhang
This paper introduces the MeshAC package, which generates three-dimensional adaptive meshes tailored for the efficient and robust implementation of multiscale coupling methods. While Delaunay triangulation is commonly used for mesh generation across the entire computational domain, generating meshes for multiscale coupling methods is more challenging due to intrinsic discrete structures such as defects, and the need to match these structures to the continuum domain at the interface. The MeshAC package tackles these challenges by creating hierarchical mesh structures linked through a novel modified interface region. It also incorporates localized modification and reconstruction operations specifically designed for interfaces. These enhancements improve both the implementation efficiency and the quality of the coupled mesh. Furthermore, MeshAC introduces a novel adaptive feature that utilizes gradient-based a posteriori error estimation, which automatically adjusts the atomistic region and continuum mesh, striving for an appropriate trade-off between accuracy and efficiency. This package can be directly applied to the geometry optimization problems of a/c coupling in static mechanics [1], [2], [3], [4], [5], with potential extensions to many other scenarios. Its capabilities are demonstrated for complex material defects, including straight edge dislocation in BCC W and double voids in FCC Cu. These results suggest that MeshAC can be a valuable tool for researchers and practitioners in computational mechanics.
{"title":"MeshAC: A 3D mesh generation and adaptation package for multiscale coupling methods","authors":"Kejie Fu , Mingjie Liao , Yangshuai Wang , Jianjun Chen , Lei Zhang","doi":"10.1016/j.cpc.2025.109523","DOIUrl":"10.1016/j.cpc.2025.109523","url":null,"abstract":"<div><div>This paper introduces the <span>MeshAC</span> package, which generates three-dimensional adaptive meshes tailored for the efficient and robust implementation of multiscale coupling methods. While Delaunay triangulation is commonly used for mesh generation across the entire computational domain, generating meshes for multiscale coupling methods is more challenging due to intrinsic discrete structures such as defects, and the need to match these structures to the continuum domain at the interface. The <span>MeshAC</span> package tackles these challenges by creating hierarchical mesh structures linked through a novel modified interface region. It also incorporates localized modification and reconstruction operations specifically designed for interfaces. These enhancements improve both the implementation efficiency and the quality of the coupled mesh. Furthermore, <span>MeshAC</span> introduces a novel adaptive feature that utilizes gradient-based a posteriori error estimation, which automatically adjusts the atomistic region and continuum mesh, striving for an appropriate trade-off between accuracy and efficiency. This package can be directly applied to the geometry optimization problems of a/c coupling in static mechanics <span><span>[1]</span></span>, <span><span>[2]</span></span>, <span><span>[3]</span></span>, <span><span>[4]</span></span>, <span><span>[5]</span></span>, with potential extensions to many other scenarios. Its capabilities are demonstrated for complex material defects, including straight edge dislocation in BCC W and double voids in FCC Cu. These results suggest that <span>MeshAC</span> can be a valuable tool for researchers and practitioners in computational mechanics.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109523"},"PeriodicalIF":7.2,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143200070","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}
Pub Date : 2025-02-02DOI: 10.1016/j.cpc.2025.109531
Parikshit Boregowda , Gui-Rong Liu
Algorithms for initializing particle distribution in SPH simulations are important for improving simulation accuracy. However, no such algorithms exist for boundary integral SPH models, which can model complex geometries without requiring layers of virtual particles. This study introduces the Boundary Integral based Particle Initialization (BIPI) algorithm. It employs a particle packing algorithm meticulously designed to redistribute particles to fit the geometry boundary. The BIPI algorithm directly utilizes the geometry's boundary information using the SPH boundary integral formulation. Special consideration is given to particles adjacent to the boundary to prevent artificial volume compression. The BIPI algorithm can hence generate a particle distribution with reduced concentration gradients for domains with complex geometrical shapes. Finally, several examples are presented to demonstrate the effectiveness of the proposed algorithm, including the application of the BIPI algorithm in flow problems.
{"title":"A boundary integral based particle initialization algorithm for Smooth Particle Hydrodynamics","authors":"Parikshit Boregowda , Gui-Rong Liu","doi":"10.1016/j.cpc.2025.109531","DOIUrl":"10.1016/j.cpc.2025.109531","url":null,"abstract":"<div><div>Algorithms for initializing particle distribution in SPH simulations are important for improving simulation accuracy. However, no such algorithms exist for boundary integral SPH models, which can model complex geometries without requiring layers of virtual particles. This study introduces the Boundary Integral based Particle Initialization (BIPI) algorithm. It employs a particle packing algorithm meticulously designed to redistribute particles to fit the geometry boundary. The BIPI algorithm directly utilizes the geometry's boundary information using the SPH boundary integral formulation. Special consideration is given to particles adjacent to the boundary to prevent artificial volume compression. The BIPI algorithm can hence generate a particle distribution with reduced concentration gradients for domains with complex geometrical shapes. Finally, several examples are presented to demonstrate the effectiveness of the proposed algorithm, including the application of the BIPI algorithm in flow problems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109531"},"PeriodicalIF":7.2,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379244","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}
Pub Date : 2025-01-31DOI: 10.1016/j.cpc.2025.109530
Evgeniy Levdik , Alexey A. Shcherbakov
The rigorous solution to the grating diffraction problem is a cornerstone step in many scientific fields and industrial applications ranging from the study of the fundamental properties of metasurfaces to the simulation of photolithography masks. Fourier space methods, such as the Fourier Modal Method, are established tools for the analysis of the electromagnetic properties of periodic structures, but are too computationally demanding to be directly applied to large and multiscale optical structures. This work focuses on pushing the limits of rigorous computations of periodic electromagnetic structures by adapting a powerful tensor compression technique called the Tensor Train decomposition. We have found that the millions and billions of numbers produced by standard discretization schemes are inherently excessive for storing the information about diffraction problems required for computations with a given accuracy, and we show how to adapt the TT algorithms to have a logarithmically growing amount of information to be sufficient for reliable rigorous solution of the Maxwell's equations on an example of large period multiscale 1D grating structures.
{"title":"Logarithmically complex rigorous Fourier space solution to the 1D grating diffraction problem","authors":"Evgeniy Levdik , Alexey A. Shcherbakov","doi":"10.1016/j.cpc.2025.109530","DOIUrl":"10.1016/j.cpc.2025.109530","url":null,"abstract":"<div><div>The rigorous solution to the grating diffraction problem is a cornerstone step in many scientific fields and industrial applications ranging from the study of the fundamental properties of metasurfaces to the simulation of photolithography masks. Fourier space methods, such as the Fourier Modal Method, are established tools for the analysis of the electromagnetic properties of periodic structures, but are too computationally demanding to be directly applied to large and multiscale optical structures. This work focuses on pushing the limits of rigorous computations of periodic electromagnetic structures by adapting a powerful tensor compression technique called the Tensor Train decomposition. We have found that the millions and billions of numbers produced by standard discretization schemes are inherently excessive for storing the information about diffraction problems required for computations with a given accuracy, and we show how to adapt the TT algorithms to have a logarithmically growing amount of information to be sufficient for reliable rigorous solution of the Maxwell's equations on an example of large period multiscale 1D grating structures.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109530"},"PeriodicalIF":7.2,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143935","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}
Pub Date : 2025-01-31DOI: 10.1016/j.cpc.2025.109522
Azzeddine Tiba , Thibault Dairay , Florian De Vuyst , Iraj Mortazavi , Juan Pedro Berro Ramirez
Stable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple coupling strategies have been developed to accelerate these simulations, but often use predictors in the form of simple finite-difference extrapolations. In this work, we propose a non-intrusive data-driven predictor that couples reduced-order models of both the solid and fluid subproblems, providing an initial guess for the nonlinear problem of the next time step calculation. Each reduced order model is composed of a nonlinear encoder-regressor-decoder architecture and is equipped with an adaptive update strategy that adds robustness for extrapolation. In doing so, the proposed methodology leverages physics-based insights from high-fidelity solvers, thus establishing a physics-aware machine learning predictor. Using three strongly coupled FSI examples, this study demonstrates the improved convergence obtained with the new predictor and the overall computational speedup realized compared to classical approaches.
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Pub Date : 2025-01-30DOI: 10.1016/j.cpc.2025.109504
Bogdan A. Danciu, Christos E. Frouzakis
We present KinetiX, a software toolkit to generate computationally efficient fuel-specific routines for the chemical source term, thermodynamic and mixture-averaged transport properties for use in combustion simulation codes. The C++ routines are designed for high-performance execution on both CPU and GPU architectures. On CPUs, chemical kinetics computations are optimized by eliminating redundant operations and using data alignment and loops with trivial access patterns that enable auto-vectorization, reducing the latency of complex mathematical operations. On GPUs, performance is improved by loop unrolling, reducing the number of costly exponential evaluations and limiting the number of live variables for better register usage. The accuracy of the generated routines is checked against reference values computed using Cantera and the maximum relative errors are below . We evaluate the performance of the kernels on some of the latest CPU and GPU architectures from AMD and NVIDIA, i.e., AMD EPYC 9653, AMD MI250X, and NVIDIA H100. The routines generated by KinetiX outperform the general-purpose Cantera library, achieving speedups of up to 2.4x for species production rates and 3.2x for mixture-averaged transport properties on CPUs. Compared to the routines generated by PelePhysics (CEPTR), KinetiX achieves speedups of up to 2.6x on CPUs and 1.7x on GPUs for the species production rates kernel on a single-threaded basis.
Program summary
Program Title: KinetiX
CPC Library link to program files:https://doi.org/10.17632/cjwxfw4btt.1
Nature of problem: Combustion simulations require efficient computation of chemical source terms, thermodynamic and transport properties for diverse fuel types. The challenge is optimizing these computations for both CPUs and GPUs without compromising accuracy.
Solution method: Starting from an input file containing kinetic parameters, thermodynamic and transport data, KinetiX generates fuel-specific routines to compute species production rates, thermodynamic and mixture-averaged transport properties for high-performance execution on both CPU and GPU architectures.
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