Numerical analysis of the ostiomeatal complex aeration using the CFD method

Tretiakow, Dmitry; Tesch, K.; Markiet, Karolina; Przewoźny, Tomasz; Kusiak, Aida; Cichońska, Dominika; Skorek, Andrzej

doi:10.1038/s41598-023-31166-x

Cited by 2 publications

(2 citation statements)

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“…It can be expected that the behavior of such flow systems are highly non-linear and three-dimensional. e.g., Tretiakow et al [ 160 ] found that the flow in the ostiomeatal complex (e.g., degree of turbulence) depended on the overall geometric features of the nasal cavity (e.g., nasal septum deviation).…”

Section: Part I—computational Rhinologymentioning

confidence: 99%

“…In silico studies are further grouped according to their modelling approach, Navier-Stokes-or Lattice-Boltzmann-based, as well as the turbulence modelling approach employed (laminar, RANS, LES, DNS). The present study [118,160,172,194,223] Spalart-Allmaras [204,226,246] Reynolds stress model [83] LES and RANS-LES hybrid models [83,148,204,206,217,237,247] [159, [248][249][250] The present study DNS [83] Lattice-Boltzmann-based methods (in silico) [42,125,195,[251][252][253] [127]…”

Section: Modelling Approaches In In Silico Studiesmentioning

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: Part I—computational Rhinologymentioning

confidence: 99%

Section: Modelling Approaches In In Silico Studiesmentioning

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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show abstract

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

Johnsen

2024

Preprint

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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. The paper consists of three distinct parts – an introduction to and review of the field of computational rhinology, a review of 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 the 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.

show abstract

Numerical analysis of the ostiomeatal complex aeration using the CFD method

Cited by 2 publications

References 28 publications

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

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

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

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