Literature DB >> 24910470

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

Na Zhang1, Peng Zhang2, Wei Kang3, Danny Bluestein2, Yuefan Deng1.   

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.

Entities:  

Keywords:  Morse potential; blood plasma fluid; coarse-grained; inverse problems

Year:  2014        PMID: 24910470      PMCID: PMC4045626          DOI: 10.1016/j.jcp.2013.09.040

Source DB:  PubMed          Journal:  J Comput Phys        ISSN: 0021-9991            Impact factor:   3.553


  16 in total

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Review 6.  Plasma viscosity: a forgotten variable.

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  8 in total

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7.  Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells.

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8.  Rapid analysis of streaming platelet images by semi-unsupervised learning.

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  8 in total

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