Fibrous and Spherical Particle Transport and Deposition in the Human Nasal Airway: A Computational Fluid Dynamics Model

2009 ◽  
Author(s):  
Kevin T. Shanley ◽  
Goodarz Ahmadi ◽  
Philip K. Hopke ◽  
Yung-Sung Cheng
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Particulate Matter ◽  
Particle Tracking ◽  
Full Range ◽  
Spherical Particles ◽  
Lagrangian Particle Tracking ◽  
Nasal Breathing ◽  
Lagrangian Particle

As the interface between the human respiratory system and the environment, the nose plays many vital roles. Not the least of which is filter. Resulting from numerous natural and anthropogenic processes, particulate matter becomes airborne. Should particulate matter reach the lower portions of the respiratory tract, a host of maladies may occur. In an attempt to further understand the physics behind particulate matter transitioning from the environment into humans a computational model has been developed to predict the efficiency with which human noses can remove particles before they reach the lungs. To this end computational fluid dynamics and Lagrangian particle tracking simulations have been run to gather information on the deposition behavior of both fibrous and spherical particles. MRI data was collected from the left and right passages of a 181.6 cm, 120.2 kg, human male. The two passages were constructed into separate computational volumes consisting of approximately 950,000 unstructured tetrahedral cells each. A steady laminar flow model was used to simulate the inhalation portion of a human breathing cycle. Volumetric flow rates were varied to represent the full range of human nasal breathing. General agreement was shared quantitatively and qualitatively with previously published in vitro studies on other nasal models. Lagrangian particle tracking was performed for varying sizes of fibrous and spherical particles. Deposition efficiency was shown to increase with fiber aspect ratio, particle size, and flow rate. Anatomy was also identified as effecting deposition.

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.

Experimental and Numerical Study of Ascending Aorta Hemodynamics Through 3D Particle Tracking Velocimetry and Computational Fluid Dynamics

2013 ◽  
Author(s):  
Utku Gülan ◽  
Diego Gallo ◽  
Raffaele Ponzini ◽  
Beat Lüthi ◽  
Markus Holzner ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Numerical Study ◽  
Imaging Techniques ◽  
Ascending Aorta ◽  
Human Aorta ◽  
Wide Range

The complex hemodynamics observed in the human aorta make this district a site of election for an in depth investigation of the relationship between fluid structures, transport and pathophysiology. In recent years, the coupling of imaging techniques and computational fluid dynamics (CFD) has been applied to study aortic hemodynamics, because of the possibility to obtain highly resolved blood flow patterns in more and more realistic and fully personalized flow simulations [1]. However, the combination of imaging techniques and computational methods requires some assumptions that might influence the predicted hemodynamic scenario. Thus, computational modeling requires experimental cross-validation. Recently, 4D phase contrast MRI (PCMRI) has been applied in vivo and in vitro to access the velocity field in aorta [2] and to validate numerical results [3]. However, PCMRI usually requires long acquisition times and suffers from low spatial and temporal resolution and a low signal-to-noise ratio. Anemometric techniques have been also applied for in vitro characterization of the fluid dynamics in aortic phantoms. Among them, 3D Particle Tracking Velocimetry (PTV), an optical technique based on imaging of flow tracers successfully used to obtain Lagrangian velocity fields in a wide range of complex and turbulent flows [4], has been very recently applied to characterize fluid structures in the ascending aorta [5].

Efficiency prediction for storage chambers using computational fluid dynamics

1996 ◽  
Vol 33 (9) ◽  
pp. 163-170 ◽  
Author(s):  
Virginia R. Stovin ◽  
Adrian J. Saul
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Particle Tracking ◽  
Critical Value ◽  
Complex Geometries ◽  
Bed Shear Stress ◽  
Breadth Ratio ◽  
Computational Fluid Dynamics Cfd ◽  
Simulated Flow

Research was undertaken in order to identify possible methodologies for the prediction of sedimentation in storage chambers based on computational fluid dynamics (CFD). The Fluent CFD software was used to establish a numerical model of the flow field, on which further analysis was undertaken. Sedimentation was estimated from the simulated flow fields by two different methods. The first approach used the simulation to predict the bed shear stress distribution, with deposition being assumed for areas where the bed shear stress fell below a critical value (τcd). The value of τcd had previously been determined in the laboratory. Efficiency was then calculated as a function of the proportion of the chamber bed for which deposition had been predicted. The second method used the particle tracking facility in Fluent and efficiency was calculated from the proportion of particles that remained within the chamber. The results from the two techniques for efficiency are compared to data collected in a laboratory chamber. Three further simulations were then undertaken in order to investigate the influence of length to breadth ratio on chamber performance. The methodology presented here could be applied to complex geometries and full scale installations.

scholarly journals 3D Lagrangian particle tracking of a subsonic jet using multi-pulse Shake-The-Box

2021 ◽  
Vol 123 ◽  
pp. 110346
Author(s):  
Peter Manovski ◽  
Matteo Novara ◽  
Nagendra Karthik Depuru Mohan ◽  
Reinhard Geisler ◽  
Daniel Schanz ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Subsonic Jet
Sign in / Sign up

Export Citation Format

Share Document