Multiscale Modeling of Flow over Functionalized Surfaces: Algorithms and Applications
功能化表面流动的多尺度建模:算法和应用
基本信息
- 批准号:0852948
- 负责人:
- 金额:$ 35.67万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-15 至 2013-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
CBET - 0852948KarniadakisMany microfluidic, synthetic-materials, and biomedical applications often need to model multiscale flow phenomena across several orders of magnitude in spatio-temporal scales from near-wall subdomains but also the outer flow over long simulation times. The goal of this project is to develop a validated methodology for simulating multiscale flow phenomena over functionalized surfaces with a biomedical focus. To this end, the PIs propose a "triple-decker" flow model based on interfacing seamlessly a mesoscopic method (dissipative particle dynamics or DPD) to molecular dynamics (MD) on one side and incompressible Navier-Stokes (NS) equations on the other side. The novelty of the PIs' approach is the use of a mesoscopic layer between NS and MD--unlike previous approaches-- to facilitate a smooth transition from the atomistic to the continuum regime. Their preliminary results for simple fluids show the great potential of this method. Here the PIs propose fundamental new developments to make the method applicable to complex fluids and to flows over functionalized surfaces including polymer brushes, where an assembly of polymer chains tethered by one end to a surface creates a surface with specialized properties. The large theoretical and experimental works on this topic, starting with the work of de Gennes, will act as a testbed to validate the proposed methodology and evaluate its efficiency and then model cytoadhesion over protein-coated surfaces using the polymer brushes as model of cell surface. The objective here is to develop a molecularly based adhesive dynamics model to complement existing mechanistic macromodels for multiparticle adhesive dynamics. Specifically, the PIs will simulate the binding of malaria-infected red blood cells (RBCs) to functionalized walls, as was done in recent microfluidic experiments, in essence mimicking cytoadhesion in arterioles and capillaries. The triple-decker (MD-DPD-NS) approach is general and can be applied to simple and complex fluids in microfluidic or biomedical applications but also in more classical applications, e.g., control of wall shear stress using surfactants or hydrophobic surfaces. DPD, first popularized in Europe, is a very effective method for modelng both complex fluids and soft matter but has not yet been adapted widely in USA, and the proposed work will contribute to its further use and development. More broadly, this work on polymer brushes can be used in a wide range of industrial applications in oil recovery, automotive lubrication, colloid stabilization, and in tailoring surface properties. The PIs will disseminate their models and the triple-decker codes as open source codes via existing external open source websites. They will organize seminar-courses open to all students at Brown University focused on multiscale modeling and applications. In addition, undergraduate students, through Brown's UTRA (Undergraduate Teaching and Research Assistantships) program, will be involved in the research projects, either during the academic year or the summer. The PIs also plan outreach activities for inner-city high school students in a partnership with the MET school, where Brown students will be tutoring MET high school students in physics and mathematics in close collaboration with MET school teachers.This study is cofunded by the CBET, CMMI, and DMS divisions.
CBET-0852948KARNIADAKISMASY微流体,合成 - 材料和生物医学应用通常需要在近壁子域中的时空尺度上的几个数量级的多尺度流动现象进行模拟,但在长时间的模拟时间内,外部流量也是外部流动的。该项目的目的是开发一种经过验证的方法,用于以生物医学的重点在功能化表面上模拟多尺度流动现象。为此,PIS提出了一个基于介质方法(耗散粒子动力学或DPD)的“三甲板”流模型,一侧是分子动力学(MD),另一侧不可压缩的Navier-Stokes(NS)方程。 PIS方法的新颖性是使用NS和MD之间的介质层(类似于先前的方法),以促进从原子体到连续体制的平稳过渡。他们对简单流体的初步结果表明了这种方法的巨大潜力。在这里,PI提出了基本的新开发项目,以使该方法适用于复杂的流体,并在功能化表面上流动,包括聚合物刷,其中由一端束缚在表面上的聚合物链的组装产生具有专业特性的表面。从DE Gennes的工作开始,关于该主题的大型理论和实验著作将充当测试型,以验证所提出的方法,并评估其效率,然后使用聚合物刷作为细胞表面模型对蛋白质饰面的表面上的细胞粘附进行建模。这里的目的是开发一个基于分子的粘附动力学模型,以补充多颗粒粘合剂动力学的现有机械大型模型。具体而言,PIS将模拟疟疾感染的红细胞(RBC)与功能化壁的结合,就像最近的微流体实验中所做的那样,本质上是模仿小动脉和毛细血管中细胞粘附的。三甲基(MD-DPD-NS)方法是一般的,可以应用于微流体或生物医学应用中的简单且复杂的流体,但在更经典的应用中,例如,使用表面活性剂或疏水性表面控制壁剪应力。 DPD首先在欧洲普及,是一种非常有效的方法,用于建模复杂的液体和软物质,但尚未在美国进行广泛调整,拟议的工作将有助于其进一步的使用和开发。更广泛地说,这项在聚合物刷上的工作可用于油回收,汽车润滑,胶体稳定和调整表面特性方面的广泛工业应用。 PI将通过现有的外部开源网站将其模型和三层代码传播为开源代码。他们将对布朗大学的所有学生开放研讨会,专注于多尺度建模和应用。此外,在学年或夏季,通过Brown's UTRA(本科教学和研究助理工作)计划,本科生将参与研究项目。 PIS还计划与大都会学校的合作伙伴关系,为市中心高中生计划外展活动,在那里,棕色的学生将与Met School Scholters进行密切合作,与MET Physics and Mathematics的高中生进行辅导。这项研究由CBET,CMMI和DMS Divisions cofund。
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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George Karniadakis其他文献
Physics-Informed Learning Machines for Partial Differential Equations: Gaussian Processes Versus Neural Networks
用于偏微分方程的物理学习机:高斯过程与神经网络
- DOI:
10.1007/978-3-030-44992-6_14 - 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Guofei Pang;George Karniadakis - 通讯作者:
George Karniadakis
CMINNs: Compartment model informed neural networks — Unlocking drug dynamics
- DOI:
10.1016/j.compbiomed.2024.109392 - 发表时间:
2025-01-01 - 期刊:
- 影响因子:
- 作者:
Nazanin Ahmadi Daryakenari;Shupeng Wang;George Karniadakis - 通讯作者:
George Karniadakis
MSM White Paper: Cell Scale to Macroscale Integration
MSM 白皮书:细胞尺度到宏观尺度的集成
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Ching;George Karniadakis;James G. Brasseur;Bridget S. Wilson;Yi Jiang - 通讯作者:
Yi Jiang
Simulating and visualizing the human arterial system on the TeraGrid
- DOI:
10.1016/j.future.2006.03.019 - 发表时间:
2006-10-01 - 期刊:
- 影响因子:
- 作者:
Suchuan Dong;Joseph Insley;Nicholas T. Karonis;Michael E. Papka;Justin Binns;George Karniadakis - 通讯作者:
George Karniadakis
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
- DOI:
10.1016/j.cma.2023.116681 - 发表时间:
2024-02-15 - 期刊:
- 影响因子:
- 作者:
Ehsan Haghighat;Umair bin Waheed;George Karniadakis - 通讯作者:
George Karniadakis
George Karniadakis的其他文献
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{{ truncateString('George Karniadakis', 18)}}的其他基金
Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior
合作研究:AMPS:通过机器学习进行多保真度建模,实时预测电力系统行为
- 批准号:
1736088 - 财政年份:2017
- 资助金额:
$ 35.67万 - 项目类别:
Continuing Grant
MANNA 2017: Modeling, Analysis, and Numerics for Nonlocal Applications
MANNA 2017:非局部应用的建模、分析和数值
- 批准号:
1747867 - 财政年份:2017
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
New evolution equations of the joint response-excitation PDF for stochastic modeling: Theory and numerical methods
用于随机建模的联合响应激励 PDF 的新演化方程:理论和数值方法
- 批准号:
1216437 - 财政年份:2012
- 资助金额:
$ 35.67万 - 项目类别:
Continuing Grant
Collaborative Research: Scalable Multiscale Models for the Cerebrovasculature: Algorithms, Software and Petaflop Simulations
合作研究:可扩展的脑血管多尺度模型:算法、软件和千万亿次模拟
- 批准号:
0904288 - 财政年份:2009
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
Overcoming the Bottlenecks in Polynomial Chaos: Algorithms and Applications to Systems Biology and Fluid Mechanics
克服多项式混沌的瓶颈:系统生物学和流体力学的算法和应用
- 批准号:
0915077 - 财政年份:2009
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
Multiscale Models and Petaflops Simulations on the Human Brain Vascular Network
人脑血管网络的多尺度模型和千万亿次模拟
- 批准号:
0845449 - 财政年份:2008
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
International Conference on Spectral and High-Order Methods 2009 - ICOSAHOM'09; June 2009, Trondheim, Norway
2009 年光谱和高阶方法国际会议 - ICOSAHOM09;
- 批准号:
0839866 - 财政年份:2008
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
CI-TEAM Implementation Project: Collaborative Research: Training Simulation Scientists in Advanced Cyberinfrastructure Tools and Concepts
CI-TEAM 实施项目:协作研究:培训模拟科学家掌握先进的网络基础设施工具和概念
- 批准号:
0636336 - 财政年份:2006
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
AMC-SS: A Multi-Element Generalized Polynomial Chaos Method for Modeling Uncertainty in Flow Simulations
AMC-SS:一种用于流体仿真中不确定性建模的多元素广义多项式混沌方法
- 批准号:
0510799 - 财政年份:2005
- 资助金额:
$ 35.67万 - 项目类别:
Standard Grant
A Stochastic Molecular Dynamics Method for Multiscale Modeling of Blood Platlet Pheonmena
血小板现象多尺度建模的随机分子动力学方法
- 批准号:
0506312 - 财政年份:2005
- 资助金额:
$ 35.67万 - 项目类别:
Continuing Grant
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