Influence of Mesh Density on Airflow and Particle Deposition in Sinonasal Airway Modeling

Frank‐Ito, Dennis O.; WoffordMatthew,; SchroeterJeffry, D; KimbellJulia, S

doi:10.1089/jamp.2014.1188

Cited by 77 publications

(55 citation statements)

References 30 publications

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“…Researchers have explored a myriad of topics, such as characterizing nasal airflow in healthy subjects (Zhao and Jiang, 2014), quantifying nasal filtration of airborne particles (Frank-Ito et al, 2015; Garcia et al, 2015b; Schroeter et al, 2011; Schroeter et al, 2015), heating and humidification of inspired air (Dayal et al, 2016; Goodarzi-Ardakani et al, 2016), the relationship between nasal function and nasal anatomy (Garcia et al, 2016; Patki and Frank-Ito, 2016), and airflow in the paranasal sinuses (Zhu et al, 2012; Zhu et al, 2014). In particular, there is much interest in identifying flow variables that correlate with symptoms of nasal airway obstruction (Garcia et al, 2016; Garcia et al, 2010; Hariri et al, 2015; Kim et al, 2014; Na et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency

Gaberino

Rhee

Garcia

2017

Respiratory Physiology & Neurobiology

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INTRODUCTION The nasal cycle represents a significant challenge when comparing pre- and post-surgery objective measures of nasal airflow. METHODS Computational fluid dynamics (CFD) simulations of nasal airflow were conducted in 12 nasal airway obstruction patients showing significant nasal cycling between pre- and post-surgery computed tomography scans. To correct for the nasal cycle, mid-cycle models were created virtually. Subjective scores of nasal patency were obtained via the Nasal Obstruction Symptom Evaluation (NOSE) and unilateral visual analog scale (VAS). RESULTS The correlation between objective and subjective measures of nasal patency increased after correcting for the nasal cycle. In contrast to biophysical variables in individual patients, cohort averages were not significantly affected by the nasal cycle correction. CONCLUSIONS The ability to correct for the confounding effect of the nasal cycle is a key element that future virtual surgery planning software for nasal airway obstruction will need to account for when using anatomic models based on single instantaneous imaging.

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Section: Discussionmentioning

confidence: 99%

Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency

Gaberino

Rhee

Garcia

2017

Respiratory Physiology & Neurobiology

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“…Planar nostril and outlet surfaces, as well as the left and right maxillary sinuses regions for tracking particle deposition were constructed. Following the creation of these regions, unstructured tetrahedral meshes were generated with approximately 6 million graded elements; mesh density analysis conducted by Frank-Ito et al (49) found asymptotic behavior for both outlet flow rate and transnasal pressure drop occurred around 4 million cells. This implies that a finer grid of 6 million elements will produce consistent results with those from 4 million unstructured tetrahedral elements.…”

Section: Selection Of Study Cohortmentioning

confidence: 99%

“…This implies that a finer grid of 6 million elements will produce consistent results with those from 4 million unstructured tetrahedral elements. (49) Mesh quality analysis ensured that all tetrahedral elements had an aspect ratio greater than 0.3, to prevent distorted elements from affecting the accuracy of the numerical simulation. A finer three-layer prism-element with a 0.1 mm prism thickness for each layer was created at the sinonasal airway walls to account for near-wall particle trajectories accurately.…”

Section: Selection Of Study Cohortmentioning

confidence: 99%

“…A finer three-layer prism-element with a 0.1 mm prism thickness for each layer was created at the sinonasal airway walls to account for near-wall particle trajectories accurately. The mesh density and structure was chosen to agree with a detailed mesh refinement analysis study conducted by Frank-Ito et al (49) For this reason, grid convergence test was not done for this particular study.…”

Section: Selection Of Study Cohortmentioning

confidence: 99%

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A Computational Study of Nasal Spray Deposition Pattern in Four Ethnic Groups

Keeler

Patki

Woodard

et al. 2016

Journal of Aerosol Medicine and Pulmonary Drug Delivery

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Background: Very little is known about the role of nasal morphology due to ethnic variation on particle deposition pattern in the sinonasal cavity. This preliminary study utilizes computational fluid dynamics (CFD) modeling to investigate sinonasal airway morphology and deposition patterns of intranasal sprayed particles in the nose and sinuses of individuals from four different ethnic groups: African American (Black); Asian; Caucasian; and Latin American. Methods: Sixteen subjects (four from each ethnic group) with ''normal'' sinus protocol computed tomography (CT) were selected for CFD analysis. Three-dimensional reconstruction of each subject's sinonasal cavity was created from their personal CT images. CFD simulations were carried out in ANSYS Fluent TM in two phases: airflow phase was done by numerically solving the Navier-Stokes equations for steady state laminar inhalation; and particle dispersed phase was solved by tracking injected (sprayed) particles through the calculated airflow field. A total of 10,000 particle streams were released from each nostril, 1000 particles per diameter ranging from 5 lm to 50 lm, with size increments of 5 lm. Results: As reported in the literature, Caucasians (5.31 -0.42 cm

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“…Most of the research in this area considers a steady inlet airflow [25] or an unsteady flow representing inhalation-exhalation cycles [26]. Frank-Ito et al [27] studied the effect of the volume grid cells on the deposition efficiency of the particles while modeling the realistic airflow and particle transport in a realistic human cavity including the paranasal sinuses. They found that by increasing the number of the tetrahedral elements and specially the density of the volume grid cells at the wall, the particle deposition fraction decreased.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of the Dispersion and Deposition of a Spray Carried by a Pulsating Airflow in a Patient-Specific Human Nasal Cavity

et al. 2017

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The present numerical study concerns the dispersion and deposition of a nasal spray in a patient-specific human nose. The realistic three-dimensional geometry of the nasal cavity is reconstructed from computer tomography (CT) scans. Identification of the region of interest, removal of artifacts, segmentation, generation of the .STL file and the triangulated surface grid are performed using the software packages ImageJ, meshLab, and NeuRA2. An unstructured computational volume grid with approximately 15 million tetrahedral grid cells is generated using the software Ansys ICEM-CFD 11.0. An unsteady Eulerian-Lagrangian formulation is used to describe the airflow and the spray dispersion and deposition in the realistic human nasal airway using two-way coupling. A new solver called pimpleParcelFoam is developed, which combines the lagrangianParcel libraries with the pimpleFoam solver within the software package OpenFOAM 3.0.0. A large eddy simulation (LES) with the dynamic sub-grid scale (SGS) model is performed to study the spray in both a steady and a pulsating airflow with an inflow rate of 7.5 L/min (or maximum value in case of the pulsating spray) and a frequency of 45 Hz for pulsation as used in commercial inhalation devices. 10,000 mono-disperse particles with the diameters of 2.4 µm and 10 µm are uniformly injected at the nostrils. In order to fulfil the stability conditions for the numerical solution, a constant time-step of 10 −5 s is implemented. The simulations are performed for a real process time of 1 s, since after the first second of the process, all particles have escaped through the pharynx or they are deposited at the surface of the nasal cavity. The numerical computations are performed on the high-performance computer bwForCluster MLS&WISO Production using 256 processors, which take around 32 and 75 hours for steady and pulsating flow simulation, respectively. The study shows that the airflow velocity reaches its maximum values in the nasal valve, in parts of the septum and in the nasopharynx. A complex airflow is observed in the vestibule and in the nasopharynx region, which may directly affect the dispersion and deposition pattern of the spray. The results reveal that the spray tends to deposit in the nasal valve, the septum and in the nasopharynx due to the change in the direction of the airflow in these regions. Moreover, it is found that due to the pulsating airflow, the aerosols are more dispersed and penetrate deeper into the posterior regions and the meatuses where the connections to the sinuses reside.

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Influence of Mesh Density on Airflow and Particle Deposition in Sinonasal Airway Modeling

Abstract: To ensure numerically accurate simulation results, mesh refinement analyses should be performed for both airflow and particle transport simulations. Tetrahedral-only meshes overpredict particle deposition and are less accurate than hybrid tetrahedral-prism meshes.

Cited by 77 publications

References 30 publications

Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency

Estimates of nasal airflow at the nasal cycle mid-point improve the correlation between objective and subjective measures of nasal patency

A Computational Study of Nasal Spray Deposition Pattern in Four Ethnic Groups

Numerical Simulation of the Dispersion and Deposition of a Spray Carried by a Pulsating Airflow in a Patient-Specific Human Nasal Cavity

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