Message from the Guest Editor of the SCONA 2022 Meeting Special Issue

Inthavong, Kiao

doi:10.1007/s42757-022-0147-5

Cited by 1 publication

(4 citation statements)

References 7 publications

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“…On the other hand, this variability is an argument for employing the patient-specific diagnostics that only CFD can offer. Despite extensive work over the last decades, there is still controversy regarding best practice for CFD simulation of flow in the upper airways [83][84][85].…”

Section: Computational Fluid Dynamics In Rhinologymentioning

confidence: 99%

“…Outside the near-wall region, the spatial resolution must be sufficient to resolve all relevant flow structures. It has been suggested that 4-6 million grid cells is generally sufficient to achieve mesh independence [85,106,176], but this is a generalization that should be accepted with caution, since it might not be appropriate for all nasal geometries and volumetric flowrates.…”

Section: (A) Spatial Resolutionmentioning

confidence: 99%

“…There is an ongoing debate about the nature of the flow in the human upper airways in the scientific literature [83][84][85]. One of the main concerns has been whether the flow is laminar, transitional, or turbulent.…”

Section: Modelling Approaches In In Silico Studiesmentioning

confidence: 99%

“…While the assumption of laminar flow has traditionally dominated CFD modelling of nasal airflow (refer to Part II, Section 3), a consensus on whether its characteristic behavior is laminar, transitional, or turbulent remains elusive [83][84][85]. Published comparisons between laminar and turbulent modelling approaches have typically employed steady state RANSbased turbulence models relying on the Boussinesq hypothesis.…”

Section: Turbulence Modellingmentioning

confidence: 99%

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Computational Rhinology: Unraveling Discrepancies between In Silico and In Vivo Nasal Airflow Assessments for Enhanced Clinical Decision Support

Johnsen

2024

Bioengineering

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Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in, e.g., the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. This paper consists of three distinct parts—an introduction to and review of the field of computational rhinology, a review of the published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high-fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of this paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.

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Section: Computational Fluid Dynamics In Rhinologymentioning

confidence: 99%

Section: (A) Spatial Resolutionmentioning

confidence: 99%