Extracting signature responses from respiratory flows: Low‐dimensional analyses on Direct Numerical Simulation‐predicted wakes of a flapping uvula. (29th October 2020)

• Record Type: Journal Article
• Authors: Xi, Jinxiang; Wang, Junshi; Si, Xiuhua April; Zheng, Shaokuan; Donepudi, Ramesh; Dong, Haibo
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 36:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-29
• Subjects: continuous wavelet analysis -- DNS -- dynamic mode decomposition -- obstructive sleep apnea -- proper orthogonal decomposition -- snoring -- uvula vibration; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3406
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: Uvula‐induced snoring and associated obstructive sleep apnea is a complex phenomenon characterized by vibrating structures and highly transient vortex dynamics. This study aimed to extract signature features of uvula wake flows of different pathological origins and develop a linear reduced‐order surrogate model for flow control. Six airway models were developed with two uvula kinematics and three pharynx constriction levels. A direct numerical simulation (DNS) flow solver based on the immersed boundary method was utilized to resolve the wake flows induced by the flapping uvula. Key spatial and temporal responses of the flow to uvula kinematics and pharynx constriction were investigated using continuous wavelet transform (CWT), proper orthogonal decomposition (POD), and dynamic mode decomposition (DMD). Results showed highly complex patterns in flow topologies. CWT analysis revealed multiscale correlations in both time and space between the flapping uvular and wake flows. POD analysis successfully separated the flows among the six models by projecting the datasets in the vector space spanned by the first three eigenmodes. Perceivable differences were also captured in the time evolution of the DMD modes among the six models. A linear reduced‐order surrogate model was constructed from the predominant eigenmodes obtained from the DMD analysis and predicted vortex patterns from this surrogate model agreed well with the corresponding DNS simulations. The computational and analytical platform presented in this study could bring a variety of applications in breathing‐related disorders and beyond. The computational efficiency of surrogate modeling makes it well suited for flow control, forecasting, and uncertainty analyses. Abstract : 1. Time series input‐output (vibrating structure to flow) correlations were evaluated using continuous wavelet analysis. 2. POD and DMD analyses were performed to extract signature features in DNS‐predicted wake flows of a flapping uvula with varying vibration modes. 3. A DMD‐based linear surrogate model was developed that can be used for future state estimate (diagnosis) and flow optimization (intervention) for snoring and apnea.