No abstract

An in-house 3D fluid–structure–acoustic interaction numerical solver was employed to investigate the effect of subglottic stenosis (SGS) on dynamics of glottal flow, vocal fold vibration and acoustics during voice production. The investigation focused on two SGS properties, including severity defined as the percentage of area reduction and location. The results show that SGS affects voice production only when its severity is beyond a threshold, which is at 75% for the glottal flow rate and acoustics, and at 90% for the vocal fold vibrations. Beyond the threshold, the flow rate, vocal fold vibration amplitude and vocal efficiency decrease rapidly with SGS severity, while the skewness quotient, vibration frequency, signal-to-noise ratio and vocal intensity decrease slightly, and the open quotient increases slightly. Changing the location of SGS shows no effect on the dynamics. Further analysis reveals that the effect of SGS on the dynamics is primarily due to its effect on the flow resistance in the entire airway, which is found to be related to the area ratio of glottis to SGS. Below the SGS severity of 75%, which corresponds to an area ratio of glottis to SGS of 0.1, changing the SGS severity only causes very small changes in the area ratio; therefore, its effect on the flow resistance and dynamics is very small. Beyond the SGS severity of 75%, increasing the SGS severity, leads to rapid increases of the area ratio, resulting in rapid changes in the flow resistance and dynamics.

A hydrodynamic/acoustic splitting method was used to examine the effect of supraglottal acoustics on fluid–structure interactions during human voice production in a two-dimensional computational model. The accuracy of the method in simulating compressible flows in typical human airway conditions was verified by comparing it to full compressible flow simulations. The method was coupled with a three-mass model of vocal fold lateral motion to simulate fluid–structure interactions during human voice production. By separating the acoustic perturbation components of the airflow, the method allows isolation of the role of supraglottal acoustics in fluid–structure interactions. The results showed that an acoustic resonance between a higher harmonic of the sound source and the first formant of the supraglottal tract occurred during normal human phonation when the fundamental frequency was much lower than the formants. The resonance resulted in acoustic pressure perturbation at the glottis which was of the same order as the incompressible flow pressure and found to affect vocal fold vibrations and glottal flow rate waveform. Specifically, the acoustic perturbation delayed the opening of the glottis, reduced the vertical phase difference of vocal fold vibrations, decreased flow rate and maximum flow deceleration rate (MFDR) at the glottal exit; yet, they had little effect on glottal opening. The results imply that the sound generation in the glottis and acoustic resonance in the supraglottal tract are coupled processes during human voice production and computer modeling of vocal fold vibrations needs to include supraglottal acoustics for accurate predictions.

This study presents a novel method that combines a computational fluid-structure interaction model with an interpretable deep-learning model to explore the fundamental mechanisms of seal whisker sensing. By establishing connections between crucial signal patterns, flow characteristics, and attributes of upstream obstacles, the method has the potential to enhance our understanding of the intricate sensing mechanisms. The effectiveness of the method is demonstrated through its accurate prediction of the location and orientation of a circular plate placed in front of seal whisker arrays. The model also generates temporal and spatial importance values of the signals, enabling the identification of significant temporal-spatial signal patterns crucial for the network’s predictions. These signal patterns are further correlated with flow structures, allowing for the identification of important flow features relevant for accurate prediction. The study provides insights into seal whiskers’ perception of complex underwater environments, inspiring advancements in underwater sensing technologies.