Particle laden flows - theory, analysis and experiment
颗粒负载流 - 理论、分析和实验
基本信息
- 批准号:1312543
- 负责人:
- 金额:$ 28万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-01 至 2017-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The research project synthesizes theory, computation and experiments to create multiscale models of dense particulate flows, ranging from microstructural dynamics of nearly contacting particles to the macrorheological properties of dense suspensions. The theoretical core of the project includes both mechanistic models of nearly contacting groups of particles, and continuum modeling and analysis of the dynamics of dense suspensions. The research develops new Stokesian Dynamics algorithms to resolve the slow particle interactions in dense suspensions, and uses simulations of how particles collectively reconfigure on the micro-scale to accommodate applied external strains to construct a continuum model for suspension rheology. New theory is supported by experiments carried out in the Applied Mathematics Laboratory at UCLA to test predictive models of suspension rheology.Particle laden flows are important in many industrial applications including oil and gas extraction, coal processing, mining of minerals, and waste-water treatment. Results from this basic research project can be used to develop quantitative models for such diverse applications as spiral separators in the mining industry, cell separation in inertial microfluidic devices and aggregation of microbes. The project supports both graduate student research and undergraduate research involving experiments and theory.
该研究项目综合了理论,计算和实验,以创建致密颗粒流的多尺度模型,从几乎接触颗粒的微观结构动力学到致密悬浮液的宏观流变特性。该项目的理论核心包括近接触颗粒群的机械模型,以及稠密悬浮液动力学的连续建模和分析。该研究开发了新的斯托克斯动力学算法来解决稠密悬浮液中的缓慢颗粒相互作用,并使用模拟颗粒如何在微观尺度上集体重新配置以适应施加的外部应变,以构建悬浮液流变学的连续模型。新的理论得到了UCLA应用数学实验室的实验支持,以测试悬浮液流变学的预测模型。颗粒负载流在许多工业应用中非常重要,包括石油和天然气开采、煤炭加工、矿物开采和废水处理。 该基础研究项目的结果可用于开发各种应用的定量模型,如采矿业中的螺旋分离器,惯性微流体装置中的细胞分离和微生物聚集。该项目支持研究生研究和涉及实验和理论的本科生研究。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Andrea Bertozzi其他文献
Incorporating Texture Features into Optical Flow for Atmospheric Wind Velocity Estimation
将纹理特征纳入光流中进行大气风速估计
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Joel Barnett;Andrea Bertozzi;L. Vese;Igor Yanovsky - 通讯作者:
Igor Yanovsky
Encased Cantilevers and Alternative Scan Algorithms for Ultra-Gantle High Speed Atomic Force Microscopy
- DOI:
10.1016/j.bpj.2011.11.3193 - 发表时间:
2012-01-31 - 期刊:
- 影响因子:
- 作者:
Paul Ashby;Dominik Ziegler;Andreas Frank;Sindy Frank;Alex Chen;Travis Meyer;Rodrigo Farnham;Nen Huynh;Ivo Rangelow;Jen-Mei Chang;Andrea Bertozzi - 通讯作者:
Andrea Bertozzi
Andrea Bertozzi的其他文献
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{{ truncateString('Andrea Bertozzi', 18)}}的其他基金
Collaborative Research: RAPID: Rapid computational modeling of wildfires and management with emphasis on human activity
合作研究:RAPID:野火和管理的快速计算建模,重点关注人类活动
- 批准号:
2345256 - 财政年份:2023
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
ATD: Active Learning Activity Detection in Multiplex Networks of Geospatial-Cyber-Temporal Data
ATD:地理空间网络时空数据多重网络中的主动学习活动检测
- 批准号:
2318817 - 财政年份:2023
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Collaborative Research: Differential Equations Motivated Multi-Agent Sequential Deep Learning: Algorithms, Theory, and Validation
协作研究:微分方程驱动的多智能体序列深度学习:算法、理论和验证
- 批准号:
2152717 - 财政年份:2022
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
RAPID: Analysis of Multiscale Network Models for the Spread of COVID-19
RAPID:针对 COVID-19 传播的多尺度网络模型分析
- 批准号:
2027438 - 财政年份:2020
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
FRG: Collaborative Research: Robust, Efficient, and Private Deep Learning Algorithms
FRG:协作研究:稳健、高效、私密的深度学习算法
- 批准号:
1952339 - 财政年份:2020
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
ATD: Algorithms for Threat Detection in Knowledge Graphs
ATD:知识图中的威胁检测算法
- 批准号:
2027277 - 财政年份:2020
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
NRT-HDR: Modeling and Understanding Human Behavior: Harnessing Data from Genes to Social Networks
NRT-HDR:建模和理解人类行为:利用从基因到社交网络的数据
- 批准号:
1829071 - 财政年份:2018
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
ATD: Sparsity Models for Forecasting Spatio-Temporal Human Dynamics
ATD:预测时空人类动力学的稀疏模型
- 批准号:
1737770 - 财政年份:2017
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Extreme-scale algorithms for geometric graphical data models in imaging, social and network science
成像、社会和网络科学中几何图形数据模型的超大规模算法
- 批准号:
1417674 - 财政年份:2014
- 资助金额:
$ 28万 - 项目类别:
Continuing Grant
Collaborative Research: Modeling, Analysis, and Control of the Spatio-temporal Dynamics of Swarm Robotic Systems
协作研究:群体机器人系统时空动力学的建模、分析和控制
- 批准号:
1435709 - 财政年份:2014
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
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