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Collaborative Research: Efficient Modeling of Incompressible Fluid Dynamics at Moderate Reynolds Numbers by Deconvolution LES Filters Analysis and Applications to Hemodynamics

Collaborative Research: Efficient Modeling of Incompressible Fluid Dynamics at Moderate Reynolds Numbers by Deconvolution LES Filters Analysis and Applications to Hemodynamics
合作研究:通过反卷积 LES 滤波器分析和在血流动力学中的应用,对中等雷诺数下的不可压缩流体动力学进行有效建模
批准号:
1620406
负责人:
Alessandro Veneziani
金额:
$18.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-07-01 至 2019-06-30

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中文摘要
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英文摘要
Computational fluid dynamics has emerged as a powerful tool to study the physiopathology of the cardiovascular system and for patient-specific Surgical Planning (SP) for cardiovascular diseases. Recently, clinical trials - the standard procedure for understanding diseases and assessing the impact of therapies and devices in the clinical practice - have been supported by a massive use of numerical simulations to improve the knowledge extracted from measured data, leading to Computer Aided Clinical Trials (CACT). For a large variety of pathologies involving the aorta - the major artery of the circulation - this requires to work with turbulent flows. While Direct Numerical Simulation in this context can be appropriate for a proof of concept, for the large number of patients involved in CACT and SP we need different numerical tools to provide the appropriate trade-off between accuracy and reliability needed by clinical applications and computational efficiency needed by tight deadlines. As CACT and SP are new emerging concepts in cardiovascular mathematics, an appropriate numerical modeling of turbulent physiological flows for clinical applications is now an unmet need that we intend to solve in this proposal.A possible way to limit the computational costs associated with Direct Numerical Simulations without sacrificing accuracy is to solve the flow average and model properly the effects of the small scales (not directly solved) at the medium and large scales (solved). We intend to investigate carefully new cutting-edge methods for disturbed flows based on Large Eddy Simulation (LES) Deconvolution filtering techniques with the ultimate goal of enabling practical use of numerical tools to improve knowledge extraction and clinical practice through CACT and SP. The main objective of this research is the development and the analysis of a robust and accurate LES based approach requiring no or minimal user's set-up for realistic incompressible flow problems with application to computational hemodynamics. We articulate the project in the following points: (a) Sensitivity analysis of key parameters involved in the method to understand their impact on the solution, leading to an automated parameter set-up through physical and numerical arguments. (b) Development and analysis of high-order in time methods, particularly for the computation of the pressure, with consequent improvement of the mass conservation properties. (c) Analysis of the impact of our LES approach on non-Dirichlet boundary conditions and the possible backflow stabilizing effects. We plan to test the method on both academic and real bioengineering problems. Finally, we plan to deliver a finite element open source library incorporating the findings of our research, available for CACT and SP.
期刊论文(2)
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DOI: 10.1016/j.jcp.2019.109103
发表时间: 2020-03
期刊: J. Comput. Phys.
影响因子: --
作者: [Huijuan Xu;Francesca Di Massimo;D. Baroli;A. Quaini;A. Veneziani]
通讯作者: Huijuan Xu;Francesca Di Massimo;D. Baroli;A. Quaini;A. Veneziani
Global Sensitivity Analysis for Patient-Specific Aortic Simulations: The Role of Geometry, Boundary Condition and Large Eddy Simulation Modeling Parameters
针对特定患者主动脉模拟的全局敏感性分析:几何形状、边界条件和大涡模拟建模参数的作用
DOI: 10.1115/1.4048336
发表时间: 2021
期刊: Journal of Biomechanical Engineering
影响因子: --
作者: [Xu, Huijuan, Baroli, Davide, Veneziani, Alessandro]
通讯作者: Veneziani, Alessandro
Collaborative Research: Data-Driven Variational Multiscale Reduced Order Models for Biomedical and Engineering Applications
  • 批准号:
    2012686
  • 项目类别:
    Standard Grant
  • 资助金额:
    $22.62万
  • 财政年份:
    2020
  • 负责人:
    Alessandro Veneziani
  • 依托单位:
Hierarchical model reduction techniques for incompressible fluid dynamics and fluid-structure interaction problems
  • 批准号:
    1419060
  • 项目类别:
    Standard Grant
  • 资助金额:
    $24.84万
  • 财政年份:
    2014
  • 负责人:
    Alessandro Veneziani
  • 依托单位:
Collaborative Research: Novel Data Assimilation Techniques in Mathematical Cardiology-Development, Analysis and Validation
  • 批准号:
    1412973
  • 项目类别:
    Standard Grant
  • 资助金额:
    $15.0万
  • 财政年份:
    2014
  • 负责人:
    Alessandro Veneziani
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)