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今日/当前日期收录 1 信号源:cs.SE, cs.CL, cs.AI, cs.LG, cs.PL
2602.15149 2026-06-18 cs.CE cs.NA math.NA 版本更新 60%

SoliDualSPHysics: An extension of DualSPHysics for solid mechanics with hyperelasticity, plasticity, and fracture

SoliDualSPHysics:一种用于固体力学的DualSPHysics扩展,支持超弹性、塑性及断裂

Mohammad Naqib Rahimi, George Moutsanidis

专题命中 其他AI编程 :开源软件扩展,涉及代码但非AI编程核心

AI总结 本文提出SoliDualSPHysics,一种基于SPH的开源软件,扩展DualSPHysics以模拟超弹性、有限应变塑性及脆性断裂行为,采用总拉格朗日格式,支持动态加载下的裂纹萌生与扩展,验证了其准确性和可扩展性。

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AI中文摘要

我们介绍了SoliDualSPHysics,一种新颖的开源且基于GPU加速的软件,扩展DualSPHysics以实现超弹性、有限应变塑性及脆性断裂行为的数值模拟。该软件实现了总拉格朗日格式,允许直接应用外部载荷和边界条件,支持独立的固体力学模拟。脆性断裂通过相场方法与SPH耦合,允许在动态加载下实现裂纹萌生、扩展和分叉,无需额外标准或局部细化。框架还支持用户定义的数学表达式来规定时间与空间相关的量,补充了固体力学和断裂扩展,并增强了现有和未来DualSPHysics应用的灵活性。利用DualSPHysics原生的CPU/GPU并行架构,该软件在大规模模拟中实现了显著的计算加速,且通过基准数值问题和实验数据验证了其准确性、鲁棒性和良好的扩展性能。提供了全面的实现细节和用户文档,以确保可重复性和支持社区进一步开发。框架和源代码通过公共GitHub仓库免费提供。

英文摘要

We introduce SoliDualSPHysics, a novel open-source and GPU-accelerated software that extends DualSPHysics to enable the numerical simulation of hyperelastic, finite-strain plastic, and brittle fracture behavior in deformable solids within a unified smoothed particle hydrodynamics (SPH) software framework. The software implements a total Lagrangian formulation for solid mechanics that allows direct application of external loads and boundary conditions, enabling independent solid mechanics simulations. Brittle fracture is modeled through a phase-field approach coupled with SPH, allowing crack initiation, propagation, and branching under dynamic loading without explicit crack tracking, ad hoc crack-path criteria, or local refinement. The framework also supports user-defined mathematical expressions to prescribe time- and space-dependent quantities, complementing the solid and fracture extensions and enhancing flexibility across existing and future DualSPHysics applications. Leveraging DualSPHysics' native CPU/GPU parallel architecture, the software achieves substantial computational acceleration for large-scale simulations, and the implementation is verified and validated against benchmark numerical problems and experimental data, demonstrating accuracy, robustness, and favorable scaling performance. Comprehensive implementation details and user documentation are provided to ensure reproducibility and to support further development by the community. The framework and source code are freely available through a public GitHub repository.