Evaluation of upper airway characteristics in patients with and without sleep apnea using cone-beam computed tomography and computational fluid dynamics.

OBJECTIVE: To determine if upper airway characteristics and airway pressure change significantly between low risk, healthy non-OSA subjects, and OSA subjects during respiration using cone-beam computed tomography (CBCT) imaging and steady-state k-ω model computational fluid dynamics (CFD) fluid flow simulations, respectively. MATERIALS AND METHODS: CBCT scans were collected at both end-inhalation and end-exhalation for 16 low-risk non-OSA subjects and compared to existing CBCT data from 7 OSA subjects. The CBCT images were imported into Dolphin Imaging and the upper airway was segmented into stereolithography (STL) files for area and volumetric measurements. Subject models that met pre-processing criteria underwent CFD simulations using ANSYS Fluent Meshing (Canonsburg, PA) in which unstructured mesh models were generated to solve the standard dual equation turbulence model (k-ω). Objective and supplemental descriptive measures were obtained and statistical analyses were performed with both parametric and non-parametric tests to evaluate statistical significance at P < .05. RESULTS: Regarding area and volumetric assessments, there were statistically significant mean differences in Total Volume and Minimum CSA between non-OSA and OSA groups at inhalation and exhalation (P = .002, .003, .004, and .007), respectively. There were also statistically significant mean differences in volume and min CSA between the inhalation and exhalation for the non-OSA group (P < .001 and .002), respectively. CONCLUSION: While analysis of the CFD simulation was limited by the collected data available, a finding consistent with published literature was that the OSA subject group simulation models depicted the point of lowest pressure coinciding with the area of maximum constriction.

Computer simulation of the SARS-CoV-2 contamination risk in a large dental clinic.

COVID-19, caused by the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus, has been rapidly spreading worldwide since December 2019, causing a public health crisis. Recent studies showed SARS-CoV-2's ability to infect humans via airborne routes. These motivated the study of aerosol and airborne droplet transmission in a variety of settings. This study performs a large-scale numerical simulation of a real-world dentistry clinic that contains aerosol-generating procedures. The simulation tracks the dispersion of evaporating droplets emitted during ultrasonic dental scaling procedures. The simulation considers 25 patient treatment cubicles in an open plan dentistry clinic. The droplets are modeled as having a volatile (evaporating) and nonvolatile fraction composed of virions, saliva, and impurities from the irrigant water supply. The simulated clinic's boundary and flow conditions are validated against experimental measurements of the real clinic. The results evaluate the behavior of large droplets and aerosols. We investigate droplet residence time and travel distance for different droplet diameters, surface contamination due to droplet settling and deposition, airborne aerosol mass concentration, and the quantity of droplets that escape through ventilation. The simulation results raise concerns due to the aerosols' long residence times (averaging up to 7.31 min) and travel distances (averaging up to 24.45 m) that exceed social distancing guidelines. Finally, the results show that contamination extends beyond the immediate patient treatment areas, requiring additional surface disinfection in the clinic. The results presented in this research may be used to establish safer dental clinic operating procedures, especially if paired with future supplementary material concerning the aerosol viral load generated by ultrasonic scaling and the viral load thresholds required to infect humans.

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations.

Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: "Overview first, zoom and filter, then details on demand". However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.