CFD Transient Simulation of a Breathing Cycle in an Oral-Nasal Extrathoracic Model

Paz, Carlos; Suárez, Eduardo; Concheiro, M.; Porteiro, Jacobo

doi:10.18869/acadpub.jafm.73.240.25348

Cited by 7 publications

(4 citation statements)

References 26 publications

(29 reference statements)

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“… What is the role of the oral cavity? Paz et al [ 172 ] investigated the distribution between nasal and oral breathing under steady and unsteady flow. Chen et al [ 173 ] concluded that the inclusion of the oral cavity in CFD simulations of steady and unsteady nasal cavity flow had very little impact.…”

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%

See 1 more Smart Citation

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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“…The following initial and border conditions were considered: ▪ Air assumed as incompressible fluid [9]. Figure 4 shows that the results are starting to converge towards a single PD value.…”

Section: Figure 2 Scheme Of the Orifice Plate Theoretical Modelmentioning

confidence: 99%

A Low-Cost Air Flow Sensor/Transducer for Medical Applications: Design and Experimental Characterization

Calderón¹,

Rincón²,

Agreda³

et al. 2021

Preprint

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Mechanical ventilation systems, which are used for breathing support when a person is not able to do it by their own, requires a device for measuring the air flow to the patient in order to monitoring and a assure the magnitude establish by a medical staff. Flow sensors are the conventional devices used for the air flow measuring; however, there were not available in Peru, because of the international demand during COVID-19 pandemic. In this sense, a novel air flow sensor based on orifice plate and an intelligent transducer stage were developed as an integrated design. Advanced methodologies in simulations and experiments using specially designed equipment for this application were carried out. The obtained data was used for a mathematical characterization and dimensions validation of the integrated design. The device was tested in its real working conditions, it was implemented in a breathing circuit connected to a low-cost mechanical ventilation system based on cams. Results indicate that the designed air flow sensor/transducer is a low-cost complete medical device for mechanical ventilators able to provide satisfactorily all the ventilation parameters air flow, pressure and volume over time by measuring the air flow and calculating the others. Furthermore, this device provides directly a filtered equivalent electrical signal for a display or a computer.

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“…• What is the role of the oral cavity? Paz et al [172] investigated the distribution between nasal and oral breathing under steady and unsteady flow. Chen et al [173] concluded that the inclusion of the oral cavity in CFD simulations of steady and unsteady nasal cavity flow had very little impact.…”

mentioning

confidence: 99%

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

Johnsen

2024

Preprint

View full text Add to dashboard Cite

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

CFD Transient Simulation of a Breathing Cycle in an Oral-Nasal Extrathoracic Model

Cited by 7 publications

References 26 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

A Low-Cost Air Flow Sensor/Transducer for Medical Applications: Design and Experimental Characterization

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

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