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 -0852948 Karniadakis许多微流体、合成材料和生物医学应用通常需要对来自近壁子域的时空尺度上的几个数量级的多尺度流动现象以及长模拟时间内的外部流动进行建模。本项目的目标是开发一种有效的方法,用于模拟生物医学重点功能化表面上的多尺度流动现象。为此,PI提出了一种“三层”流动模型,该模型基于介观方法(耗散粒子动力学或DPD)与分子动力学(MD)的无缝连接,另一方面是不可压缩的Navier-Stokes(NS)方程。PI方法的新奇在于在NS和MD之间使用了一个介观层--与以前的方法不同--以促进从原子态到连续态的平稳过渡。他们对简单流体的初步结果显示了这种方法的巨大潜力。在这里,PI提出了基本的新的发展,使该方法适用于复杂的流体和流动的功能化表面,包括聚合物刷,其中聚合物链的组装拴在一个端部的表面创建一个表面与专门的属性。关于这个主题的大量理论和实验工作,从de Gennes的工作开始,将作为一个测试平台来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来模拟蛋白质包被表面上的细胞粘附。这里的目标是开发一个分子为基础的粘合剂动力学模型,以补充现有的多粒子粘合剂动力学的机理宏模型。具体来说,PI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,本质上模拟了小动脉和毛细血管中的细胞粘附。三层(MD-DPD-NS)方法是通用的,并且可以应用于微流体或生物医学应用中的简单和复杂流体,但也可以应用于更经典的应用中,例如,使用表面活性剂或疏水表面控制壁面剪切应力。DPD是一种在欧洲推广的模拟复杂流体和软物质的有效方法,但在美国尚未得到广泛应用,本文的工作将有助于它的进一步应用和发展。更广泛地说,这项关于聚合物刷的工作可用于石油回收、汽车润滑、胶体稳定和定制表面性能等广泛的工业应用。专业研究员将通过现有的外部开放源码网站将其模型和三层代码作为开放源码代码进行传播。他们将组织面向布朗大学所有学生的多尺度建模和应用课程。此外,本科生,通过布朗的UTRA(本科教学和研究助理)计划,将参与研究项目,无论是在学年或夏季。PI还计划与MET学校合作为市中心高中学生开展外展活动,布朗大学的学生将与MET学校的教师密切合作,辅导MET高中学生的物理和数学。这项研究由CBET,CMMI和DMS部门共同资助。
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
Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
- DOI:
10.1007/s00466-019-01747-7 - 发表时间:
2019-06-28 - 期刊:
- 影响因子:3.800
- 作者:
Wing Kam Liu;George Karniadakis;Shaoqiang Tang;Julien Yvonnet - 通讯作者:
Julien Yvonnet
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
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
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
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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