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Human phonation involves the flow-induced vibrations of the vocal folds (VFs) that result from the interaction with airflow through the larynx. Most voice dysfunctions correspond with the fluid–structure interaction (FSI) features as well as the local changes in perfusion within the VF tissue. This study aims to develop a multiphysics computational framework to simulate the interstitial fluid flow dynamics in vibrating VFs using a biphasic description of the tissue and FSI methodology. The integration of FSI and a permeable VF model presents a novel approach to capture phonation physics' complexity and investigate VF tissue's porous nature. The glottal airflow is modeled by the unsteady, incompressible Navier–Stokes equations, and the Brinkman equation is employed to simulate the flow through the saturated porous medium of the VFs. The computational model provides a prediction of tissue deformation metrics and pulsatile glottal flow, in addition to the interstitial fluid velocity and flow circulation within the porous structure. Furthermore, the model is used to characterize the effects of variation in subglottal lung pressure and VF permeability coefficient by conducting parametric studies. Subsequent investigations to quantify the relationships between these input variables, flow perfusion, pore pressure, and vibration amplitude are presented. A linear relationship is found between the vibration amplitude, pore pressure, and filtration flow with subglottal pressure, whereas a nonlinear dependence between the filtration velocity and VF permeability coefficient is detected. The outcomes highlight the importance of poroelasticity in phonation models.
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A Deep-Learning Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation
Abstract This paper proposes a deep-learning based generalized empirical flow model (EFM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution. A fully-connected deep neural network (DNN) is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN based EFM is coupled with a finite-element method (FEM) based solid dynamics solver for flow-structure-interaction (FSI) simulation of phonation. The EFM is evaluated by comparing to the N-S solutions in both static glottal shapes and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.
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- PAR ID:
- 10338511
- Date Published:
- Journal Name:
- Journal of Biomechanical Engineering
- ISSN:
- 0148-0731
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4053862
