Collaborative Research: Robust and Scalable Methods for Simulation and Data-Driven Modeling of Particulate Flows

协作研究：用于颗粒流模拟和数据驱动建模的稳健且可扩展的方法

• 批准号: 1821334
• 负责人: Denis Zorin
• 金额: $ 15万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821334&HistoricalAwards=false
• 关键词: Collaborative; Research; Robust; Scalable; Methods

Complex fluids are mixtures of homogeneous fluids and deformable or rigid particles or fibers. These fluids are ubiquitous in biological systems and engineering applications and accurate prediction of their behavior is important for a broad set of problems such as hemodynamics modeling, simulation of intracellular processes, and microfluidics device modeling. Currently, accurate models of complex fluids require highly expensive simulations at microscopic or mesoscopic level, resolving the behavior of each particle (e.g., a deforming cell or fiber) and their interactions. In contrast, macroscopic continuum models for complex fluids (when available) allow for a far more efficient computation. However, these models often lack accuracy, and cannot capture all important aspects of the flow behavior in realistic settings; hindering construction of fast predictive models. The goal of the project is to enable construction of such models in a data-driven way. The investigators will develop a framework based on microscopic numerical simulations. The challenge in developing efficient and accurate continuum computational models of such fluids is the hard-to-analyze transition from microscopic to macroscopic parameters. In this work, the investigators focus on (1) robust and scalable solvers for particulate flow in 2D and 3D; and (2) the utilization of our framework to explore data-driven macroscopic models for the complex flows in 2D, with an initial exploration of extensions to 3D. The team will develop the algorithms for accurate solution of large-scale 3D flows with a high volume fraction of immersed deformable particles handling viscosity contrast, complex geometric boundaries, and long simulation times. Key mathematical components for this include boundary integral formulation and computation, kernel-independent quadrature schemes for singular and near-singular integration on surfaces in 3D, contact handling and close interaction, and improving efficiency of time-stepping schemes. In the second component of the project, the investigators will apply this framework to explore macroscopic characteristics of complex flows and their dependence on microscopic parameters and local geometry; this will create a foundation for data-driven continuum models for complex fluids.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.

复杂的流体是均质流体和可变形或刚性颗粒或纤维的混合物。这些流体在生物系统和工程应用中无处不在，并且对其行为的准确预测对于诸如血液动力学建模，细胞内过程的模拟和微流体设备建模等广泛问题很重要。当前，复杂流体的准确模型需要在显微镜或介质水平上昂贵的模拟，从而解决了每个粒子的行为（例如，变形的细胞或纤维）及其相互作用。 相反，复杂流体的宏观连续模型（如果有）可以进行更有效的计算。但是，这些模型通常缺乏准确性，并且无法捕获现实环境中流动行为的所有重要方面。阻碍快速预测模型的结构。该项目的目的是以数据驱动的方式构建此类模型。研究人员将基于微观数值模拟开发一个框架。 开发此类流体的有效和准确的连续计算模型的挑战是从微观参数到宏观参数的难以分析。在这项工作中，研究人员专注于（1）2D和3D中颗粒流的稳健和可扩展的求解器； （2）利用我们的框架来探索数据驱动的宏观模型，以在2D中进行复杂流，并初步探索了3D的扩展。 该团队将开发算法，以精确解决大规模3D流动的溶液，并具有高体积的浸泡变形颗粒处理粘度对比度，复杂的几何边界和较长的仿真时间的高体积分数。 此的关键数学组成部分包括边界积分公式和计算，与3D表面上奇异和近乎单明一度整合的内核独立的正交方案，接触处理和密切相互作用以及提高时间步长方案的效率。在项目的第二部分中，研究人员将应用此框架来探索复杂流的宏观特征及其对微观参数和局部几何形状的依赖。这将为复杂流体的数据驱动的连续模型创造基础。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来评估的。

项目成果

A robust solver for elliptic PDEs in 3D complex geometries

用于 3D 复杂几何形状中椭圆偏微分方程的强大求解器

• DOI: 10.1016/j.jcp.2021.110511
• 发表时间: 2021
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Morse, Matthew J.;Rahimian, Abtin;Zorin, Denis
• 通讯作者: Zorin, Denis

Scalable simulation of realistic volume fraction red blood cell flows through vascular networks

• DOI: 10.1145/3295500.3356203
• 发表时间: 2019-09
• 期刊: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Libin Lu;Matthew J. Morse;Abtin Rahimian;G. Stadler;D. Zorin

Optimizing contact-based assemblies

• DOI: 10.1145/3478513.3480552
• 发表时间: 2021-12
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Davi C. Tozoni;Yu-Nong Zhou;D. Zorin