Computational aeroacoustics of human phonation

Šidlof, Petr; Zörner, Stefan

doi:10.1051/epjconf/20134501085

Cited by 11 publications

(5 citation statements)

References 46 publications

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“…Yet, it has been demonstrated that flow changes from laminar to transitional or turbulent only at high airflow rates, such as that seen during physical activity. 61,62,68 Furthermore, it has been demonstrated that for moderate airflow rates, simulated nasal resistances and CFD models results with or without turbulence components are very similar. 1,2 Finally, the settings for the CFD analysis, namely the air temperature and humidity, the density and viscosity of the airflow, and the negative pressure assumed at the nasopharynx, are approximations of the normal breathing conditions.…”

Section: Limitations Of Cfd Analysismentioning

confidence: 89%

“…The airflow is assumed to be laminar according to data that have demonstrated nasal airflow to be almost exclusively laminar during resting breathing, only changing to transitional or turbulent at high flow rates. 1,61,62 Using particle image velocimetry and flow visualization, Doorly et al demonstrated that airflow is laminar at quiet breathing flow rates. 40 Furthermore, good agreement was found between CFD simulations using laminar airflow and in vitro measurements at airflow rates up to 250 mL/second.…”

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confidence: 99%

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An Overview of Computational Fluid Dynamics Preoperative Analysis of the Nasal Airway

et al. 2021

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Evaluation of the nasal airway is crucial for every patient with symptoms of nasal obstruction as well as for every patient with other nasal symptoms. This assessment of the nasal airway comprises clinical examination together with imaging studies, with the correlation between findings of this evaluation and symptoms reported by the patient being based on the experience of the surgeon. Measuring nasal airway resistance or nasal airflow can provide additional data regarding the nasal airway, but the benefit of these objective measurements is limited due to their lack of correlation with patient-reported evaluation of nasal breathing. Computational fluid dynamics (CFD) has emerged as a valuable tool to assess the nasal airway, as it provides objective measurements that correlate with patient-reported evaluation of nasal breathing. CFD is able to evaluate nasal airflow and measure variables such as heat transfer or nasal wall shear stress, which seem to reflect the activity of the nasal trigeminal sensitive endings that provide sensation of nasal breathing. Furthermore, CFD has the unique capacity of making airway analysis of virtual surgery, predicting airflow changes after trial virtual modifications of the nasal airway. Thereby, CFD can assist the surgeon in deciding surgery and selecting the surgical techniques that better address the features of each specific nose. CFD has thus become a trend in nasal airflow assessment, providing reliable results that have been validated for analyzing airflow in the human nasal cavity. All these features make CFD analysis a mainstay in the armamentarium of the nasal surgeon. CFD analysis may become the gold standard for preoperative assessment of the nasal airway.

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Section: Limitations Of Cfd Analysismentioning

confidence: 89%

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confidence: 99%

An Overview of Computational Fluid Dynamics Preoperative Analysis of the Nasal Airway

et al. 2021

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“…ANSYS Fluent™ uses the finite volume method in cell-centered approach to numerically solve the discretized Navier-Stokes equations. Since the flow velocities through the nasal passage during low to moderate breathing rate usually have a Mach number ≪ 0.2, (27) the conservation of mass and momentum governing equations for laminar, incompressible steady state flow reduces to ∇⋅u→=0, ρ(u→⋅∇)u→=−∇p+μ∇2u→, where u→=u→(x,y,z) is the velocity vector, ρ = 1.204 kg/m 3 is fluid density, μ = 1.825 × 10 ‒5 kg/m ‒ s is dynamic viscosity, and ρ is pressure. The thermal energy equation for heat transfer is defined as ρcp(u→⋅∇)T=k∇2T, where T = T ( x,y,z ) is temperature, k = 0.0268 W/m-K is thermal conductivity, c p = 1005.9 J/kg-K is specific heat of air.…”

Section: Methodsmentioning

confidence: 99%

A hierarchical stepwise approach to evaluate nasal patency after virtual surgery for nasal airway obstruction

Frank‐Ito

Kimbell

Borojeni

et al. 2019

Clinical Biomechanics

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Background: Despite advances in medicine and expenditures associated in treatment of nasal airway obstruction, 25–50% of patients undergoing nasal surgeries complain of persistent obstructive symptoms. Our objective is to develop a “stepwise virtual surgery” method that optimizes surgical outcomes for treatment of nasal airway obstruction. Methods: Pre-surgery radiographic images of two subjects with nasal airway obstruction were imported into Mimics imaging software package for three-dimension reconstruction of the airway. A hierarchical stepwise approach was used to create seven virtual surgery nasal models comprising individual (inferior turbinectomy or septoplasty) procedures and combined inferior turbinectomy and septoplasty procedures via digital modifications of each subject’s pre-surgery nasal model. To evaluate the effects of these procedures on nasal patency, computational fluid dynamics modeling was used to perform steady-state laminar inspiratory airflow and heat transfer simulations in every model, at resting breathing. Airflow-related variables were calculated for virtual surgery models and compared with dataset containing results of healthy subjects with no symptoms of nasal obstruction. Findings: For Subject 1, nasal models with virtual septoplasty only and virtual septoplasty plus inferior turbinectomy on less obstructed side were within the healthy reference thresholds on both sides of the nasal cavity and across all three computed variables. For Subject 2, virtual septoplasty plus inferior turbinectomy on less obstructed side model produced the best result. Interpretation: The hierarchical stepwise approach implemented in this preliminary report demonstrates computational fluid dynamics modeling ability to evaluate the efficiency of different surgical procedures for nasal obstruction in restoring nasal patency to normative level.

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“…ANSYS Fluent uses the finite volume method to numerically solve the discretized Navier–Stokes equations. Because the flow velocities through the nasal passage during low-to-moderate breathing rate usually have a Mach number , 45 the conservation of mass and momentum governing equations for laminar, incompressible steady-state flow reduces to …”

Section: Methodsmentioning

confidence: 99%

Computational Analysis of the Mature Unilateral Cleft Lip Nasal Deformity on Nasal Patency

Frank‐Ito

Carpenter

Cheng

et al. 2019

Plastic and Reconstructive Surgery - Global Open

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Background: Nasal airway obstruction (NAO) due to nasal anatomic deformities is known to be more common among cleft patients than the general population, yet information is lacking regarding severity and variability of cleft-associated nasal obstruction relative to other conditions causing NAO. This preliminary study compares differences in NAO experienced by unilateral cleft lip nasal deformity (uCLND) subjects with noncleft subjects experiencing NAO. Methods: Computational modeling techniques based on patient-specific computed tomography images were used to quantify the nasal airway anatomy and airflow dynamics in 21 subjects: 5 healthy normal subjects; 8 noncleft NAO subjects; and 8 uCLND subjects. Outcomes reported include Nasal Obstruction Symptom Evaluation (NOSE) scores, cross-sectional area, and nasal resistance. Results: uCLND subjects had significantly larger cross-sectional area differences between the left and right nasal cavities at multiple cross sections compared with normal and NAO subjects. Median and interquartile range (IQR) NOSE scores between NAO and uCLND were 75 (IQR = 22.5) and 67.5 (IQR = 30), respectively. Airflow partition difference between both cavities were: median = 9.4%, IQR = 10.9% (normal); median = 31.9%, IQR = 25.0% (NAO); and median = 29.9%, IQR = 44.1% (uCLND). Median nasal resistance difference between left and right nasal cavities were 0.01 pa.s/ml (IQR = 0.03 pa.s/ml) for normal, 0.09 pa.s/ml (IQR = 0.16 pa.s/ml) for NAO and 0.08 pa.s/ml (IQR = 0.25 pa.s/ml) for uCLND subjects. Conclusions: uCLND subjects demonstrated significant asymmetry between both sides of the nasal cavity. Furthermore, there exists substantial disproportionality in flow partition difference and resistance difference between cleft and noncleft sides among uCLND subjects, suggesting that both sides may be dysfunctional.

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Computational aeroacoustics of human phonation

Cited by 11 publications

References 46 publications

An Overview of Computational Fluid Dynamics Preoperative Analysis of the Nasal Airway

An Overview of Computational Fluid Dynamics Preoperative Analysis of the Nasal Airway

A hierarchical stepwise approach to evaluate nasal patency after virtual surgery for nasal airway obstruction

Computational Analysis of the Mature Unilateral Cleft Lip Nasal Deformity on Nasal Patency

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