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: 2153863
• 负责人: Chenfanfu Jiang
• 金额: $ 25万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153863&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.

自然现象的计算模拟是自然科学（环境建模，地震或雪崩预测），生物力学（建模肌肉骨骼系统，器官功能或组织损伤），制造（产品设计，原型设计和验证）以及计算机图形/动画中无处不在的工具。该项目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，使用称为材料点方法（MPM）的技术推进模拟科学。该项目的增强功能将使地形动力学研究，车辆和农业机具与复杂土壤相互作用的设计和原型制作，材料失效和断裂场景建模，包括皮肤撕裂，手术切口或血管破裂等生物环境。该项目将MPM模拟扩展到：a）具有受热力学影响的多相相互作用的材料;和B）具有复杂多尺度几何特征的介质，包括孔隙率（例如水-土相互作用）或由非球形几何颗粒主导的聚集体。该项目将MPM模拟扩展到包括复杂摩擦接触（超越传统MPM中嵌入的无滑动接触模型），动态裂纹扩展和脱粘的现象。该研究线程利用了该团队先前在非流形数据结构上的工作，用于存储隐式几何表示，允许MPM的背景网格包含比传统基于阵列的规则网格更丰富的拓扑特征集（例如撕裂和切口）。最后，这项研究提高了MPM模拟的规模，可以容纳在现代多处理器中，提高了细节，分辨率以及并行效率。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

An Efficient B-Spline Lagrangian/Eulerian Method for Compressible Flow, Shock Waves, and Fracturing Solids

• DOI: 10.1145/3519595
• 发表时间: 2022-02
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Yadi Cao;Yunuo Chen;Minchen Li;Yin Yang;Xinxin Zhang;Mridul Aanjaneya;Chenfanfu Jiang

Second-order Stencil Descent for Interior-point Hyperelasticity

• DOI: 10.1145/3592104
• 发表时间: 2023-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: L. Lan;Minchen Li;Chenfanfu Jiang;Huamin Wang;Yin Yang

A novel discretization and numerical solver for non-fourier diffusion

• DOI: 10.1145/3414685.3417863
• 发表时间: 2020-11
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Tao Xue;Haozhe Su;Chengguizi Han;Chenfanfu Jiang;Mridul Aanjaneya

Multi-Layer Thick Shells

• DOI: 10.1145/3588432.3591489
• 发表时间: 2023-07
• 期刊: ACM SIGGRAPH 2023 Conference Proceedings
• 作者: Yunuo Chen;Tianyi Xie;Cem Yuksel;D. Kaufman;Yin Yang;Chenfanfu Jiang;Minchen Li

Energetically consistent inelasticity for optimization time integration

• DOI: 10.1145/3528223.3530072
• 发表时间: 2022-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Xuan Li;Minchen Li;Chenfanfu Jiang