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Research Initiation Award: Fast Solvers for Variable-Coefficient Poroelastic Models

Research Initiation Award: Fast Solvers for Variable-Coefficient Poroelastic Models
研究启动奖:变系数多孔弹性模型的快速求解器
批准号:
1700328
负责人:
Mingchao Cai
金额:
$29.93万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-05-01 至 2023-08-31

项目摘要

项目成果

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中文摘要
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英文摘要
Research Initiation Awards provide support for faculty at Historically Black Colleges and Universities who are building a research program. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at his home institution, and involves undergraduate students in research experiences. The award to Morgan State University has potential broader and societal impact in a number of areas. The project focuses on the development of numerical methods arising from many important applications. The research crosses the fields of mathematics, computational physics and material sciences. Undergraduate and graduate students will gain research experiences.This project focuses on the development of numerical methods for variable-coefficient poroelastic models. The research will propose, analyze and implement several fast, optimal and scalable numerical algorithms for poroelastic models under the spatial discretizations including the finite element method, finite volume method and immersed interface method. Specifically, novel numerical approaches, such as preconditioning methods, Multigrid methods, domain decomposition methods, and the acceleration techniques for the conventional iterative methods, will be developed and investigated. The numerical algorithms will greatly improve the efficiency of poroelastic solvers. The research has the potential of improving the linear and nonlinear solvers for the conventional Finite Difference and Finite Element methods for poroelastic models in various applications. As an example, the developed numerical algorithms can be used in simulating energy storage in subsurface, which requires large-scale numerical computations; furthermore, the developed numerical methods can be applied to simulate a brain swelling model and therefore quantify brain edema assessment. Combined with image data and patient-specific data such as cerebral blood flow conditions, the numerical methods can be used for simulating brain swelling under ischemic conditions or after traumatic brain injury. The algorithms will be implemented as open source software packages.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
An H(div)-conforming finite element Method for the Biot consolidation model
Biot固结模型的符合H(div)的有限元方法
DOI: 10.4208/eajam.170918.261218
发表时间: 2019
期刊: East Asian Journal on Applied Mathematics
影响因子: 1.2
作者: [Zeng Yuping, Cai Mingchao, Wang Feng]
通讯作者: Wang Feng
Parameter-robust multiphysics algorithms for Biot model with application in brain edema simulation
Biot模型参数鲁棒多物理场算法在脑水肿模拟中的应用
DOI: 10.1016/j.matcom.2020.04.027
发表时间: 2020
期刊: Mathematics and computers in simulation
影响因子: 4.6
作者: [Ju, Guoliang, Cai, Mingchao, Li, Jingzhi, Tian, Jing]
通讯作者: Tian, Jing
An H(div)-conforming Finite Element Method for Biot’s Consolidation Model
Biot’s 固结模型的符合 H(div) 的有限元方法
DOI: --
发表时间: 2019
期刊: East Asian Journal on Applied Mathematics
影响因子: 1.2
作者: [Zeng, Yuping, Cai, Mingchao, Wang, Feng.]
通讯作者: Wang, Feng.
Comparisons of Some Iterative Algorithms for Biot Equations.
Biot 方程一些迭代算法的比较。
DOI: --
发表时间: 2015
期刊: International journal of evolution equations
影响因子: --
作者: [Cai,Mingchao, Zhang,Guoping]
通讯作者: Zhang,Guoping
9
    CBMS Conference: Deep Learning and Numerical Partial Differential Equations
    • 批准号:
      2228010
    • 项目类别:
      Standard Grant
    • 资助金额:
      $3.5万
    • 财政年份:
      2023
    • 负责人:
      Mingchao Cai
    • 依托单位:
    Excellence in Research: Numerical Algorithms for Fluid Poroelastic Structure Interaction Models
    • 批准号:
      1831950
    • 项目类别:
      Standard Grant
    • 资助金额:
      $24.99万
    • 财政年份:
      2018
    • 负责人:
      Mingchao Cai
    • 依托单位:
    海外基金