{"title":"A conservative constrained clustering-merging algorithm for particle-in-cell codes","authors":"Dong-sheng Cai, Ping-yang Wang","doi":"10.1016/j.cpc.2025.109621","DOIUrl":null,"url":null,"abstract":"<div><div>The particle merging algorithm enables particle-in-cell codes to simulate the process of rapidly increasing particle numbers. Dividing particles that are close in phase space into a subset for merging is beneficial for preserving the particle distribution function (PDF). However, larger subsets can cause particles with significant differences to be grouped together. To address this issue, we proposed a conservative constrained clustering-merging algorithm which employs the constrained k-means method to keep the number of particles within each subset at a low level while meeting the requirement of conserving physical quantities. Subsequently, the particles in each subset are merged by probabilistically adjusting their weights. The impact of subset size on the merging results and computational performance is also discussed.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"313 ","pages":"Article 109621"},"PeriodicalIF":3.4000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Physics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010465525001237","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The particle merging algorithm enables particle-in-cell codes to simulate the process of rapidly increasing particle numbers. Dividing particles that are close in phase space into a subset for merging is beneficial for preserving the particle distribution function (PDF). However, larger subsets can cause particles with significant differences to be grouped together. To address this issue, we proposed a conservative constrained clustering-merging algorithm which employs the constrained k-means method to keep the number of particles within each subset at a low level while meeting the requirement of conserving physical quantities. Subsequently, the particles in each subset are merged by probabilistically adjusting their weights. The impact of subset size on the merging results and computational performance is also discussed.
期刊介绍：
The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper.
Computer Programs in Physics (CPiP)
These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged.
Computational Physics Papers (CP)
These are research papers in, but are not limited to, the following themes across computational physics and related disciplines.
mathematical and numerical methods and algorithms;
computational models including those associated with the design, control and analysis of experiments; and
algebraic computation.
Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.
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