Computational modelling of nasal respiratory flow. Issue 4 (29th June 2020)

• Record Type: Journal Article
• Authors: Calmet, H.; Inthavong, K.; Owen, H.; Dosimont, D.; Lehmkuhl, O.; Houzeaux, G.; Vázquez, M.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 24:Issue 4(2021)
• Page Start: 440
• Page End: 458
• Publication Date: 2020-06-29
• Subjects: CFD -- human nasal cavity -- LES -- airways; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2020.1833865
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: CFD has emerged as a promising diagnostic tool for clinical trials, with tremendous potential. However, for real clinical applications to be useful, overall statistical findings from large population samples (e.g., multiple cases and models) are needed. Fully resolved solutions are not a priority, but rather rapid solutions with fast turn-around times are desired. This leads to the issue of what are the minimum modelling criteria for achieving adequate accuracy in respiratory flows for large-scale clinical applications, with a view to rapid turnaround times. This study simulated a highly-resolved solution using the large eddy simulation (LES) method as a reference case for comparison with lower resolution models that included larger time steps and no turbulence modelling. Differences in solutions were quantified by pressure loss, flow resistance, unsteadiness, turbulence intensity, and hysteresis effects from multiple cycles. The results demonstrated that sufficient accuracy could be achieved with lower resolution models if the mean flow was considered. Furthermore, to achieve an established transient result unaffected by the initial start-up quiescent effects, the results need to be taken from at least the second respiration cycle. It was also found that the exhalation phase exhibited strong turbulence. The results are expected to provide guidance for future modelling efforts for clinical and engineering applications requiring large numbers of cases using simplified modelling approaches.