Large-scale CFD simulations of the transitional and turbulent regime for the large human airways during rapid inhalation

Calmet, Hadrien; Gambaruto, Alberto; Bates, Alister J.; Vázquez, Mariano; Houzeaux, Guillaume; Doorly, D. J.

doi:10.1016/j.compbiomed.2015.12.003

Cited by 98 publications

(77 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis of the motion of the jaw, tongue, pharyngeal walls, soft palate, and epiglottis revealed how each region of anatomy influences the volume of the airspace and the cross‐sectional area of the upper airway along its length. Many studies have previously shown the relationship between airway cross‐sectional area and airway resistance, with narrower sections increasing the subject's work of breathing. Therefore, mapping the dynamic behavior of the upper airway cross‐section throughout a breath is vital to accurately predicting the airway pressure losses in computational and experimental models.…”

Section: Discussionmentioning

confidence: 99%

“…The air was considered incompressible and isothermal at 37°C. In addition to solving the equations for the conservation of mass and momentum, large eddy simulation (LES) was employed to model sub‐grid scale turbulence as previous studies have shown this technique to be effective at modeling the turbulent and transitional flow regimes within the airway . The procedure to solve the Navier‐Stokes equations in a moving mesh was implemented as previously described .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non‐rigid registration of dynamic and static MRI

Bates

Schuh

McConnell

et al. 2018

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

Computational fluid dynamics (CFD) simulations of airflow in the human airways have the potential to provide a great deal of information that can aid clinicians in case management and surgical decision making, such as airway resistance, energy expenditure, airflow distribution, heat and moisture transfer, and particle deposition, as well as the change in each of these due to surgical interventions. However, the clinical relevance of CFD simulations has been limited to date, as previous models either did not incorporate neuromuscular motion or any motion at all. Many common airway pathologies, such as obstructive sleep apnea (OSA) and tracheomalacia, involve large movements of the structures surrounding the airway, such as the tongue and soft palate. Airway wall motion may be due to many factors including neuromuscular motion, internal aerodynamic forces, and external forces such as gravity. Therefore, to realistically model these airway diseases, a method is required to derive the airway wall motion, whatever the cause, and apply it as a boundary condition to CFD simulations. This paper presents and validates a novel method of capturing in vivo motion of airway walls from magnetic resonance images with high spatiotemporal resolution, through a novel combination of non-rigid image, surface, and surface-normal-vector registration. Coupled with image-synchronous pneumotachography, this technique provides the necessary boundary conditions for dynamic CFD simulations of breathing, allowing the effect of the airway's complex motion to be calculated for the first time, in both normal subjects and those with conditions such as OSA.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non‐rigid registration of dynamic and static MRI

Bates

Schuh

McConnell

et al. 2018

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…By imposing the same inflow for each nostril, the differences between the left/right cross-sections H. Calmet et al Journal of Aerosol Science 115 (2018) 12-28 explains these elevated velocities. These higher local flow rates are due to the small cross-sectional area of the nasal valve leading to a jet like flow (Calmet et al, 2016;Doorly, Taylor, Gambaruto, Schroter, & Tolley, 2008). These narrow undulated and intricate passageways produce drastically H. Calmet et al Journal of Aerosol Science 115 (2018) 12-28 different flow fields.…”

Section: Airflow Field Resultsmentioning

confidence: 99%

Subject-variability effects on micron particle deposition in human nasal cavities

Calmet

Kleinstreuer

Houzeaux

et al. 2018

Journal of Aerosol Science

View full text Add to dashboard Cite

A B S T R A C TValidated computer simulations of the airflow and particle dynamics in human nasal cavities are important for local, segmental and total deposition predictions of both inhaled toxic and therapeutic particles. Considering three, quite different subject-specific nasal airway configurations, micron-particle transport and deposition for low-to-medium flow rates have been analyzed. Of special interest was the olfactory region from which deposited drugs could readily migrate to the central nervous system for effective treatment. A secondary objective was the development of a new dimensionless group with which total particle deposition efficiency curves are very similar for all airway models, i.e., greatly reducing the impact of intersubject variability. Assuming dilute particle suspensions with inhalation flow rates ranging from 7.5 to 20 L/min, the airflow and particle-trajectory equations were solved in parallel with the in-house, multi-purpose Alya program at the Barcelona Supercomputing Center. The geometrically complex nasal airways generated intriguing airflow fields where the three subject models exhibit among them both similar as well as diverse flow structures and wall shear stress distributions, all related to the coupled particle transport and deposition. Nevertheless, with the new Stokes-Reynolds-number group, Stk Re 1.23 1.28 , the total deposition-efficiency curves for all three subjects and flow rates almost collapsed to a single function. However, local particle deposition efficiencies differed significantly for the three subjects when using particle diameters d p = 2, 10, and m 20 μ . Only one of the three subject-specific olfactory regions received, at relatively high values of the inertial parameter d Q p 2 , some inhaled microspheres. Clearly, for drug delivery to the brain via the olfactory region, a new method of directional inhalation of nanoparticles would have to be implemented.

show abstract

“…They found that the complex flows were produced from the transient flow over a wide range of Reynolds numbers. From their study, it was found that the turbulent level is higher in the throat than in the nose [89].…”

Section: Applicationsmentioning

confidence: 97%

“…In order to carry out the simulation, 110,000 time-steps were required on 480 cores that required~20 hours, and 20,000-30,000 mesh elements were used on each core to maintain a good parallel efficiency [88]. Calmet et al (2016) conducted the large-scale CFD stimulation of the transitional and turbulent flow by using two supercomputers to solve the transient Navier-Strokes equation [89]. The lung geometry in the study composed of an exterior face and the airways from the nasal cavity to the third lung bifurcation.…”

Section: Applicationsmentioning

confidence: 99%

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

et al. 2020

View full text Add to dashboard Cite

The measurement of deposited aerosol particles in the respiratory tract via in vivo and in vitro approaches is difficult due to those approaches' many limitations. In order to overcome these obstacles, different computational models have been developed to predict the deposition of aerosol particles inside the lung. Recently, some remarkable models have been developed based on conventional semi-empirical models, one-dimensional whole-lung models, three-dimensional computational fluid dynamics models, and artificial neural networks for the prediction of aerosol-particle deposition with a high accuracy relative to experimental data. However, these models still have some disadvantages that should be overcome shortly. In this paper, we take a closer look at the current research trends as well as the future directions of this research area.Atmosphere 2020, 11, 137 2 of 27 PM Cd , and PM Pb ), the annual limit values are 6, 5, and 500 ng/m 3 , respectively (Directives 1999/30/EC and 2008/50/EC) [9]. Inhaled drug therapy can be a useful option against many lung diseases such as asthma or COPD. The efficiency of pharmaceutical aerosols depends on their deposition in the airways, especially the upper generations [10]. The estimation of aerosol transportation and deposition in the human lung can therefore indicate strategies for the prevention or alleviation of health effects from inhaled toxic particles or new drug-aerosol delivery approaches that can overcome the limitations of inhalers [11]. Atmospheric-aerosol deposition in the human respiratory tract (RT) can be directly measured by monitoring and comparing inhaled and exhaled particle concentrations. However, due to experimental limitations, the regional dose in the respiratory system is hard to measure experimentally but can be predicted by the application of different mathematical models. Suitable mathematical models are those that show a reasonable correlation between results obtained from modeling and empirical measurements [12]. Computational models generally consist of three elements: (1) a mathematical formulation that describes the relevant physical and chemical processes, (2) the setting of specific initial and boundary conditions, and (3) the solving of equations for the specified geometry. The accuracy of a computational model depends on the extent to which these elements are realistic. Additionally, regardless of the computational model that is applied, four types of input data are required: (1) lung morphometry, (2) breathing pattern, (3) particle properties, and (4) gas/vapor properties (Figure 1) [4]. The effects of these input data to the computational models were introduced in the review of Rostami (2009) and Hussain et al. (2011) [4,13]. In this review, we only present the advances of lung geometry models due to their strong relations to the accuracies of the computational models [4,13].

show abstract

Large-scale CFD simulations of the transitional and turbulent regime for the large human airways during rapid inhalation

Cited by 98 publications

References 33 publications

A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non‐rigid registration of dynamic and static MRI

A novel method to generate dynamic boundary conditions for airway CFD by mapping upper airway movement with non‐rigid registration of dynamic and static MRI

Subject-variability effects on micron particle deposition in human nasal cavities

Prediction of Aerosol Deposition in the Human Respiratory Tract via Computational Models: A Review with Recent Updates

Contact Info

Product

Resources

About