scholarly journals Colloid particle transport in a microcapillary: NMR study of particle and suspending fluid dynamics

2016 ◽  
Vol 153 ◽  
pp. 165-173 ◽  
Author(s):  
Einar O. Fridjonsson ◽  
Joseph D. Seymour
Keyword(s):  
Fluid Dynamics ◽  
Particle Transport ◽  
Colloid Particle ◽  
Nmr Study

scholarly journals Integration of fast fluid dynamics and Markov chain model for predicting transient particle transport in buildings

2019 ◽  
Vol 111 ◽  
pp. 04030
Author(s):  
Wei Liu ◽  
Chun Chen
Keyword(s):  
Fluid Dynamics ◽  
Markov Chain ◽  
Particle Transport ◽  
Markov Chain Model ◽  
Lagrangian Model ◽  
Air Distribution ◽  
Chain Model ◽  
Indoor Environments ◽  
Eulerian Model ◽  
Simulation Tools

Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used a case from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markovchain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the studied case, the FFD-Markov-chain model was 4.7 times faster than the CFD-Eulerian model, and it was 137.4 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments.

Computational Fluid Dynamics Simulations of Particle Deposition in Large-Scale, Multigenerational Lung Models

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
D. Keith Walters ◽  
William H. Luke
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Particle Tracking ◽  
Particle Transport ◽  
Particle Deposition ◽  
Large Scale ◽  
Human Lung ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Cfd Simulations

Computational fluid dynamics (CFD) has emerged as a useful tool for the prediction of airflow and particle transport within the human lung airway. Several published studies have demonstrated the use of Eulerian finite-volume CFD simulations coupled with Lagrangian particle tracking methods to determine local and regional particle deposition rates in small subsections of the bronchopulmonary tree. However, the simulation of particle transport and deposition in large-scale models encompassing more than a few generations is less common, due in part to the sheer size and complexity of the human lung airway. Highly resolved, fully coupled flowfield solution and particle tracking in the entire lung, for example, is currently an intractable problem and will remain so for the foreseeable future. This paper adopts a previously reported methodology for simulating large-scale regions of the lung airway (Walters, D. K., and Luke, W. H., 2010, “A Method for Three-Dimensional Navier–Stokes Simulations of Large-Scale Regions of the Human Lung Airway,” ASME J. Fluids Eng., 132(5), p. 051101), which was shown to produce results similar to fully resolved geometries using approximate, reduced geometry models. The methodology is extended here to particle transport and deposition simulations. Lagrangian particle tracking simulations are performed in combination with Eulerian simulations of the airflow in an idealized representation of the human lung airway tree. Results using the reduced models are compared with those using the fully resolved models for an eight-generation region of the conducting zone. The agreement between fully resolved and reduced geometry simulations indicates that the new method can provide an accurate alternative for large-scale CFD simulations while potentially reducing the computational cost of these simulations by several orders of magnitude.

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