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The cross-flow vortex-induced vibration (VIV) response of an elastically mounted idealized undulatory seal whisker (USW) shape is investigated in a wide range of reduced velocity at angles of attack (AOAs) from 0° to 90° and a low Reynolds number of 300. The mass ratio is set to 1.0 to represent the real seal whisker. Dynamic mode decomposition is used to investigate the vortex shedding mode in various cases. In agreement with past studies, the VIV response of the USW is highly AOA-dependent because of the change in the underlying vortex dynamics. At zero AOA, the undulatory shape leads to a hairpin vortex mode that results in extremely low lift force oscillation with a lowered frequency. The frequency remains unaffected by VIV throughout the tested range of reduced velocity. As the AOA deviates from zero, alternating shedding of spanwise vortices becomes dominant. A mixed vortex shedding mode is observed at AOA = 15° in the transition. As the AOA deviated from zero, the VIV amplitude increases rapidly by two orders, reaching the maximum of about 3 times diameter at 90°. An infinite lock-in branch is present for AOA from 60° to 90°, where the VIV amplitude remains high regardless of the increase in reduced velocity. 

Abstract Fish fin rays constitute a sophisticated control system for ray-finned fish, facilitating versatile locomotion within complex fluid environments. Despite extensive research on the kinematics and hydrodynamics of fish locomotion, the intricate control strategies in fin-ray actuation remain largely unexplored. While deep reinforcement learning (DRL) has demonstrated potential in managing complex nonlinear dynamics; its trial-and-error nature limits its application to problems involving computationally demanding environmental interactions. This study introduces a cutting-edge off-policy DRL algorithm, interacting with a fluid–structure interaction (FSI) environment to acquire intricate fin-ray control strategies tailored for various propulsive performance objectives. To enhance training efficiency and enable scalable parallelism, an innovative asynchronous parallel training (APT) strategy is proposed, which fully decouples FSI environment interactions and policy/value network optimization. The results demonstrated the success of the proposed method in discovering optimal complex policies for fin-ray actuation control, resulting in a superior propulsive performance compared to the optimal sinusoidal actuation function identified through a parametric grid search. The merit and effectiveness of the APT approach are also showcased through comprehensive comparison with conventional DRL training strategies in numerical experiments of controlling nonlinear dynamics. 

In this study, the effects of antagonistic muscle actuation on the propulsion of a bilaminar-structure fish fin ray were investigated using a two-dimensional computational flow–structure interaction (FSI) model. The structure and material properties of the model were based on the realistic biological data of the sunfish fin. The effect of muscle actuation was modelled using root displacement offset between the two hemitrichs. Parametric FSI simulations were conducted by assuming a sinusoidal function of the offset over a cycle and varying the amplitude and phase difference between the actuations and pitching/plunging motions. The results show that the phase of muscle actuation is a critical factor affecting its effects. Three performance regions can be identified with different phase ranges, including a thrust-favour region, an efficiency-favour region and a thrust-efficiency-unfavour region. In each region, the relationships among the root actuations, fin-ray kinematics, vortex dynamics and resulting performance are studied and discussed. Furthermore, a strong positive correlation between the trailing–leading amplitude ratio and thrust coefficient as well as a negative relationship between the efficiency and angle of attack at the centre of mass of the fin ray are observed. 

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. 

Abstract Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid physics-informed neural network algorithm that infers 3D flow-induced tissue dynamics and other physical quantities from sparse 2D images. The algorithm combines a recurrent neural network model of soft tissue with a differentiable fluid solver, leveraging prior knowledge in solid mechanics to project the governing equation on a discrete eigen space. The algorithm uses a Long-short-term memory-based recurrent encoder-decoder connected with a fully connected neural network to capture the temporal dependence of flow-structure-interaction. The effectiveness and merit of the proposed algorithm is demonstrated on synthetic data from a canine vocal fold model and experimental data from excised pigeon syringes. The results showed that the algorithm accurately reconstructs 3D vocal dynamics, aerodynamics, and acoustics from sparse 2D vibration profiles. 

A computational framework is proposed for virtual optimization of implant configurations of type 1 thyroplasty based on patient-specific laryngeal structures reconstructed from MRI images. Through integration of a muscle mechanics-based laryngeal posturing model, a flow-structure-acoustics interaction voice production model, a real-coded genetic algorithm, and virtual implant insertion, the framework acquires the implant configuration that achieves the optimal acoustic objectives. The framework is showcased by successfully optimizing an implant that restores acoustic features of a diseased voice resulted from unilateral vocal fold paralysis (UVFP) in producing a sustained vowel utterance. The sound intensity is improved from 62 dB (UVFP) to 81 dB (post-correction). 

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.