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为血浆粗粒度建模参数化莫尔斯势。

Parameterizing the Morse Potential for Coarse-Grained Modeling of Blood Plasma.

作者信息

Zhang Na, Zhang Peng, Kang Wei, Bluestein Danny, Deng Yuefan

机构信息

Department of Applied Mathematics and Statistics, Stony Brook University, NY 11794, United States.

Department of Biomedical Engineering, Stony Brook University, NY 11790, United States.

出版信息

J Comput Phys. 2014 Jan 15;257(Pt A):726-736. doi: 10.1016/j.jcp.2013.09.040.

Abstract

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters are systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately.

摘要

诸如血液等流体的多尺度模拟是一项重大的计算挑战,需要将表征此类复杂流体的分子和宏观输运现象之间不同的时空尺度进行耦合。在本文中,通过修改传统上用于分子动力学中对振动结构建模的莫尔斯势,开发了一种粗粒度(CG)粒子模型来模拟血液流动。修改后的莫尔斯势通过有效质量尺度进行参数化,以再现血液粘性流动特性,包括人体血浆流体的密度、压力、粘度、可压缩性和特征流动动力学。参数化遵循一种标准的反问题方法,通过逐步解耦弱相关的参数空间,系统地搜索最优微观参数,以匹配粘性血液流动的宏观物理量。这种基于粒子的多尺度模型的预测与经典粘性流动解(如泊肃叶流动和库埃特流动)相比具有优势。它表明,这种粗粒度粒子模型可用于复制粘性血液流动的动力学,其优点是弥合了宏观流动尺度与表征血液流动的细胞尺度之间的差距,而基于连续介质的模型无法充分处理这一差距。

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