Importance of the numerical schemes in the CFD of the human nose

Schillaci, Andrea; Quadrio, Maurizio

doi:10.1016/j.jbiomech.2022.111100

Cited by 7 publications

(10 citation statements)

References 27 publications

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“…However, Supplementary Table S2 shows that our simulations using the SST turbulence model do not show significant differences in air conditioning with full laminar simulations we also performed for the rest situation. This result is also in line with the work of Schillaci and Quadrio, who observed similar pressure-flow relationships in the nose between laminar and SST simulations 57 .…”

Section: Methodssupporting

confidence: 92%

“…The SST model is thus suitable to study the flow in the nose 56 . As reported by Schillaci and Quadrio, the results obtained using an SST model differ only slightly from the ones obtained using Large Eddy Simulations (LES) 57 . More precisely, the centre of the nasopharynx is the only area with significant velocity differences between both approaches (and thus, on the contrary, the velocity fields in the turbinates predicted by both approaches are very close to each other).…”

Section: Methodsmentioning

confidence: 54%

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The air conditioning in the nose of mammals depends on their mass and on their maximal running speed

Rigaut,

Giaprakis,

Deruyver

et al. 2024

Sci Rep

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The nose of the mammals is responsible for filtering, humidifying, and heating the air before entering the lower respiratory tract. This conditioning avoids, notably, dehydration of the bronchial and alveolar mucosa. However, since this conditioning is not perfect, exercising in cold air can induce lung inflammation, both for human and non-human mammals. This work aims to compare the air conditioning in the noses of various mammals during inspiration. We build our study on computational fluid dynamics simulations of the heat exchanges in the lumen of the upper respiratory tract of these mammals. These simulations show that the efficiency of the air conditioning in the nose during inspiration does not relate only to the mass m of the mammal but also to its maximal running speed v. More precisely, the results allow establishing a scaling law relating the efficiency of air conditioning in the nose of mammals to the ratio $$v/\log _{10}(m)$$ v / log 10 ( m ) . The simulations also correlate the resistance to the flow in the nose to the efficiency of this air conditioning. The obtained scaling law allows predicting the air temperature at the top of the trachea during inspiration for nasal-breathing mammals, and thus notably for humans of various ages.

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Section: Methodssupporting

confidence: 92%

Section: Methodsmentioning

confidence: 54%

The air conditioning in the nose of mammals depends on their mass and on their maximal running speed

Rigaut,

Giaprakis,

Deruyver

et al. 2024

Sci Rep

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“… Inadequate spatial or temporal refinement. Poorly selected solver settings and numerical schemes [ 148 ]. Incorrect definition of flow physics, including, e.g., boundary conditions, material properties, and approximations.…”

Section: Part I—computational Rhinologymentioning

confidence: 99%

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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“…The influence of numerical schemes in CFD has been outlined by Schillaci et al 7 The authors mention that for a physiologically meaningful flow rate, changes in the global pressure drop up to more than 50% are observed; locally, velocity differences can become extremely significant.…”

Section: Introductionmentioning

confidence: 99%

“…The sensitivity of the CFD procedure to the spatial resolution of the available CT scans, as well as to the choice of the segmentation level of the CT images, have been discussed by Quadrio et al 19 An analysis of 3D models and, in particular, the shape of the meshes that define the model for CFD simulation has been presented by Inthavong et al 20 The impact of the segmentation method on the CFD results is described by Schillaci and Quadrio. 21 A virtual model of nasal airways can be used in CFD. Several studies have concerned this issue.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of virtual rhinomanometry

Karbowski

Kopiczak

Chrzan

et al. 2023

Polish Journal of Medical Physics and Engineering

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Introduction: This paper describes the results of research aimed at developing a method of otolaryngological diagnosis based on computational fluid dynamics, which has been called Virtual Rhinomanometry. Material and methods: Laboratory studies of airflows through a 3D printed model of nasal cavities based on computed tomography image analysis have been performed. The CFD results have been compared with those of an examination of airflow through nasal cavities (rhinomanometry) of a group of 25 patients. Results: The possibilities of simplifying model geometry for CFD calculations have been described, the impact of CT image segmentation on geometric model accuracy and CFD simulation errors have been analysed, and recommendations for future research have been described. Conclusions: The measurement uncertainty of the nasal cavities’ walls has a significant impact on CFD simulations. The CFD simulations better approximate RMM results of patients after anemization, as the influence of the nasal mucosa on airflow is then reduced. A minor change in the geometry of the nasal cavities (within the range of reconstruction errors by CT image segmentation) has a major impact on the results of CFD simulations.

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Importance of the numerical schemes in the CFD of the human nose

Cited by 7 publications

References 27 publications

The air conditioning in the nose of mammals depends on their mass and on their maximal running speed

The air conditioning in the nose of mammals depends on their mass and on their maximal running speed

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

Accuracy of virtual rhinomanometry

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