AF: Small: Collaborative Research: Scalable and Topologically Versatile Material Point Methods for Complex Materials in Multiphysics Simulation

AF：小型：协作研究：多物理场仿真中复杂材料的可扩展且拓扑通用的质点方法

• 批准号: 1813624
• 负责人: Chenfanfu Jiang
• 金额: $ 25万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813624&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Scalable

Computational simulation of natural phenomena is a ubiquitous tool in the natural sciences (environmental modeling, prediction of earthquakes or avalanches), bio-mechanics (modeling the musculoskeletal system, organ function, or tissue damage), manufacturing (product design, prototyping, and verification) as well as in computer graphics/animation. This project advances the science of simulation using a technique called Material Point Methods (MPM), by increasing the number of materials and range of phenomena that can be simulated, and optimizing the performance of numerical algorithms used for simulations at larger scales. The enhancements from this project will enable studies in terrain dynamics, design and prototyping of vehicles and agricultural implements interacting with complex soils, modeling of material failure and fracture scenarios including biological settings such as skin tearing, surgical incisions, or vascular rupture. The optimization and scaling will allow computational studies of simulated physical systems at levels of resolution that were previously only afforded to large enterprises.This project extends MPM simulation to: a) materials with multi-phase interactions influenced by thermodynamics; and b) media with complex multi-scale geometric features, including porosity (e.g. water-soil interactions) or aggregates dominated by grains of non-spherical geometry. This project extends MPM simulation to phenomena that include intricate frictional contact (beyond the no-slip contact model embedded in traditional MPM), dynamic crack propagation, and de-cohesion. This research thread leverages the team's prior work on non-manifold data structures for storing implicit geometry representations, allowing the background grids of MPM to incorporate a richer set of topological features (e.g. tears and incisions) than those incorporated by conventional array-based regular lattices. Finally, this research boosts the scale of MPM simulations that can be accommodated in modern multiprocessors, improving detail, resolution as well as parallel efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自然现象的计算机模拟是自然科学(环境建模、地震或雪崩预测)、生物力学(对肌肉骨骼系统、器官功能或组织损伤进行建模)、制造(产品设计、原型制作和验证)以及计算机图形学/动画中普遍使用的工具。该项目使用一种名为材质点方法(MPM)的技术，通过增加可模拟的材料数量和现象范围，并优化用于更大规模模拟的数值算法的性能，来促进模拟科学的发展。该项目的增强将使人们能够研究地形动力学、车辆和农具与复杂土壤相互作用的设计和原型、材料失效和骨折场景的建模，包括皮肤撕裂、外科手术切开或血管破裂等生物环境。该项目将MPM模拟扩展到：a)受热力学影响的多相相互作用的材料；b)具有复杂的多尺度几何特征的介质，包括孔隙度(例如水土相互作用)或由非球形几何颗粒主导的集合体。该项目将MPM模拟扩展到包括复杂摩擦接触(超出传统MPM中嵌入的无滑动接触模型)、动态裂纹扩展和解聚的现象。这一研究思路利用了该团队之前在用于存储隐式几何表示的非流形数据结构方面的工作，允许MPM的背景网格包含比传统基于数组的规则网格包含的拓扑特征集更丰富的拓扑特征(例如，撕裂和切割)。最后，这项研究扩大了现代多处理器可以容纳的MPM模拟的规模，提高了细节、分辨率和并行效率。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Massively Parallel and Scalable Multi-GPU Material Point Method

一种大规模并行、可扩展的多GPU质点方法

• DOI: 10.1145/3386569.3392442
• 发表时间: 2020-07
• 期刊: ACM TRANSACTIONS ON GRAPHICS
• 影响因子: 6.2
• 作者: Wang Xinlei;Qiu Yuxing;Slattery Stuart R.;Fang Yu;Li Minchen;Zhu Song-Chun;Zhu Yixin;Tang Min;Manocha Dinesh;Jiang Chenfanfu
• 通讯作者: Jiang Chenfanfu

Efficient and Conservative Fluids Using Bidirectional Mapping

• DOI: 10.1145/3306346.3322945
• 发表时间: 2019-07-01
• 期刊: ACM TRANSACTIONS ON GRAPHICS
• 影响因子: 6.2
• 作者: Qu, Ziyin;Zhang, Xinxin;Chen, Baoquan
• 通讯作者: Chen, Baoquan

Hierarchical Optimization Time Integration for CFL-Rate MPM Stepping

CFL 速率 MPM 步进的分层优化时间积分

• DOI: 10.1145/3386760
• 发表时间: 2019-11
• 期刊: ACM Transactions on Graphics
• 影响因子: 6.2
• 作者: Xinlei Wang;Minchen Li;Yu Fang;Xinxin Zhang;Ming Gao;Min Tang;Danny M. Kaufman;Chenfanfu Jiang
• 通讯作者: Chenfanfu Jiang

Lagrangian–Eulerian multidensity topology optimization with the material point method

• DOI: 10.1002/nme.6668
• 发表时间: 2020-03
• 期刊: International Journal for Numerical Methods in Engineering
• 影响因子: 2.9
• 作者: Yue Li;Xuan Li;Minchen Li;Yixin Zhu;Bo Zhu;Chenfanfu Jiang

PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification

• DOI: 10.48550/arxiv.2303.05512
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: Xuan Li;Yi-Ling Qiao;Peter Yichen Chen;Krishna Murthy Jatavallabhula;Ming Lin;Chenfanfu Jiang;Chuang Gan