A Parallel Mesh Generation Framework for Particle Methods with Adaptive Initialization Leveraging Central Processing Unit and Graphics Processing Unit Co-processing

A Parallel Mesh Generation Framework for Particle Methods with Adaptive Initialization Leveraging Central Processing Unit and Graphics Processing Unit Co-processing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Parallel Mesh Generation Framework for Particle Methods with Adaptive Initialization Leveraging Central Processing Unit and Graphics Processing Unit Co-processing Yuefan Hu, Honglei Bai, Yufei Pang, Guangxue Wang, Chunguang Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9285040/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In this paper, we propose a new particle-based parallel mesh generation framework leveraging Central Processing Unit and Graphics Processing Unit (CPU–GPU) co-processing, referred to as PPMGF-CG. Additionally, a novel adaptive sampling method based on local integration is designed to refine the initial particle distribution, thereby accelerating convergence toward the target size field and density field. The proposed parallel framework consists of two modules: the host side and the device side, with the algorithm comprising three core steps. Step 1. Initial Adaptive Particle Distribution: An adaptive sampling method based on local integration is applied to generate an initial particle distribution that conforms to the target size field and density field. Step 2. GPU-Parallel Particle Relaxation Using the Adaptive Smoothing Length Smoothed Particle Hydrodynamics (SPH): The adaptive smoothing length SPH is employed to iteratively adjust particle positions, aligning the particle distribution with the target density field. Step 3. Mesh Connectivity Construction via Constrained Delaunay Triangulation (CDT): CDT is utilized to establish topological connectivity among the particles, forming a high-quality triangular mesh. In the CPU-GPU co-processing framework, Steps 1 and 3 are executed on the CPU, while Step 2 is processed on the GPU, leveraging its parallel computing capabilities. Benchmark results show that the proposed adaptive sampling method significantly accelerates convergence. The CPU–GPU hybrid framework improves computational efficiency by an order-of-magnitude over CPU-only implementations. Compared with conventional CDT, the particle-based mesh generation method achieves higher mesh quality and superior efficiency in engineering applications. Particle-Based Mesh Generation Methods CPU-GPU Smoothed Particle Hydrodynamics Constrained Delaunay Triangulation Triangular Mesh Generation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 04 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 31 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9285040","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618533240,"identity":"fe02ec77-fe8b-43d1-9d71-d8fc72d0f2a0","order_by":0,"name":"Yuefan Hu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuefan","middleName":"","lastName":"Hu","suffix":""},{"id":618533241,"identity":"5d2b6a7f-67b0-4d53-aa6e-d69481f28b51","order_by":1,"name":"Honglei Bai","email":"","orcid":"","institution":"Sun Yat-sen 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