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.
more »
« less
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.
more »
« less
- NSF-PAR ID:
- 10338511
- Date Published:
- Journal Name:
- Journal of Biomechanical Engineering
- ISSN:
- 0148-0731
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case–control classification. An
L 0‐norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi‐study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols (N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools. -
Abstract While research in heritage language phonology has found that transfer from the majority language can lead to divergent attainment in adult heritage language grammars, the extent to which language transfer develops during a heritage speaker's lifespan is understudied. To explore such cross-linguistic transfer, I examine the rate of glottalization between consonant-to-vowel sequences at word junctures produced by child and adult Spanish heritage speakers (i.e., HSs) in both languages. My results show that, in Spanish, child HSs produce greater rates of vowel-initial glottal phonation than their age-matched monolingually-raised Spanish counterparts, suggesting that the Spanish child HSs’ grammars are more permeable to transfer than those of the adult HSs. In English, child and adult HSs show similarly low rates of glottal phonation when compared to their age-matched monolingually-raised English speakers’ counterparts. The findings for English can be explained by either an account of transfer at the individual level or the community level.more » « less
-
We develop a theory of fluid--structure interaction (FSI) between an oscillatory Newtonian fluid flow and a compliant conduit. We consider the canonical geometries of a 2D channel with a deformable top wall and an axisymmetric deformable tube. Focusing on the hydrodynamics, we employ a linear relationship between wall displacement and hydrodynamic pressure, which has been shown to be suitable for a leading-order-in-slenderness theory. The slenderness assumption also allows the use of lubrication theory, and the flow rate is related to the pressure gradient (and the tube/wall deformation) via the classical solutions for oscillatory flow in a channel and in a tube (attributed to Womersley). Then, by two-way coupling the oscillatory flow and the wall deformation via the continuity equation, a one-dimensional nonlinear partial differential equation (PDE) governing the instantaneous pressure distribution along the conduit is obtained, without \textit{a priori} assumptions on the magnitude of the oscillation frequency (\textit{i.e.}, at arbitrary Womersley number). We find that the cycle-averaged pressure (for harmonic pressure-controlled conditions) deviates from the expected steady pressure distribution, suggesting the presence of a streaming flow. An analytical perturbative solution for a weakly deformable conduit is obtained to rationalize how FSI induces such streaming. In the case of a compliant tube, the results obtained from the proposed reduced-order PDE and its perturbative solutions are validated against three-dimensional, two-way-coupled direct numerical simulations. We find good agreement between theory and simulations for a range of dimensionless parameters characterizing the oscillatory flow and the FSI, demonstrating the validity of the proposed theory of oscillatory flows in compliant conduits at arbitrary Womersley number.more » « less
-
This study investigates the shear rate dependent margination of micro-particles (MPs) with different shapes in blood flow through numerical simulations. We develop a multiscale computational model to handle the fluid–structure interactions involved in the blood flow simulations. The lattice Boltzmann method (LBM) is used to solve the plasma dynamics and a coarse-grained model is employed to capture the dynamics of red blood cells (RBCs) and MPs. These two solvers are coupled together by the immersed boundary method (IBM). The shear rate dependent margination of sphere MPs is firstly investigated. We find that margination of sphere MPs dramatically increases with the increment of wall shear rate ω under 800 s −1 , induced by the breaking of rouleaux in blood flow. However, the margination probability only slowly grows when ω > 800 s −1 . Furthermore, the shape effect of MPs is examined by comparing the margination behaviors of sphere-like, oblate-like and prolate-like MPs under different wall shear rates. We find that the margination of MPs is governed by the interplay of two factors: hydrodynamic collisions with RBCs including the collision frequency and collision displacement of MPs, and near wall dynamics. MPs that demonstrate poor performance in one process such as collision frequency may stand out in the other process like near wall dynamics. Specifically, the ellipsoidal MPs (oblate and prolate) with small aspect ratio (AR) outperform those with large AR regardless of the wall shear rate, due to their better performance in both the collision with RBCs and near wall dynamics. Additionally, we find there exists a transition shear rate region 700 s −1 < ω < 900 s −1 for all of these MPs: the margination probability dramatically increases with the shear rate below this region and slowly grows above this region, similar to sphere MPs. We further use the surface area to volume ratio (SVR) to distinguish different shaped MPs and illustrate their shear rate dependent margination in a contour in the shear rate–SVR plane. It is of significance that we can approximately predict the margination of MPs with a specific SVR. All these simulation results can be potentially applied to guide the design of micro-drug carriers for biomedical applications.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4053862
