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Abstract Helical blood flow, characterized by its spiral motion, is a crucial physiological phenomenon observed in various circulatory structures, including the heart, aorta, vessel bifurcations, umbilical cord, and respiratory system. Despite its importance, a comprehensive understanding of helical flow dynamics is limited. This study aims to investigate the transition from laminar to turbulent flow in helical tubes under both steady and pulsatile conditions. An experimental setup was developed, featuring a closed-loop system with a pulsatile flow generator and steady pump, producing sinusoidal waveforms with adjustable frequencies and amplitudes. Five helical tube models, varying in curvature radius and torsion, were fabricated with a fixed inner diameter of 15 mm. High-frequency pressure transducers, ultrasonic flow sensors, and Laser Doppler Velocimetry (LDV) were employed to visualize flow fields and measure turbulence kinetic energy (TKE). Results indicate that helical tube geometry significantly impacts turbulence transitions. Specifically, larger curvature radii stabilize the flow and reduce turbulence downstream, while smaller radii lead to earlier transitions to turbulence. Under steady flow conditions, the critical Reynolds number (Re) for turbulence onset was found to be around 2300 upstream, similar to straight pipes, but significantly higher turbulence levels were observed downstream. Pulsatile flow with high pulsatility indices (PI > 3) near transitional Re markedly increases turbulence, particularly downstream, with large bursts of turbulence occurring during the deceleration phase. These findings enhance the understanding of helical flow dynamics and have implications for biomedical device design, diagnostics, and treatments for circulatory disorders.
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Bidirectional Venturi Flowmeter Based on Capacitive Sensors for Spirometry
Abstract In this work, a portable venturi tube capable of measuring bidirectional respiratory flow is developed and correlated the measurements to pulmonary function. Pressure signals are transduced using flexible and compressible capacitive foam sensors embedded into the wall of the device. In this configuration, the sensors are able to provide differential pressure readings, from which the airflow rate passing through the tube could be extrapolated. Utilizing the venturi effect, the geometry of the spirometer tube is designed through finite element analysis to measure respiratory airflow during inhalation and exhalation. The device tube is 3D‐printed and used to measure tidal breathing and deep breathing, along with peak expiratory flow rates, on a healthy individual. This spirometer design allows for easy‐to‐use point‐of‐care diagnoses and has the potential to improve the care of respiratory illnesses.
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- PAR ID:
- 10454899
- Date Published:
- Journal Name:
- Advanced Materials Technologies
- ISSN:
- 2365-709X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The interactions between fluid flow and structural components of collapsible tubes are representative of those in several physiological systems. Although extensively studied, there exists a lack of characterization of the three-dimensionality in the structural deformations of the tube and its influence on the flow field. This experimental study investigates the spatio-temporal relationship between 3D tube geometry and the downstream flow field under conditions of fully open, closed, and slamming-type oscillating regimes. A methodology is implemented to simultaneously measure three-dimensional surface deformations in a collapsible tube and the corresponding downstream flow field. Stereophotogrammetry was used to measure tube deformations, and simultaneous flow field measurements included pressure and planar Particle Image Velocimetry (PIV) data downstream of the collapsible tube. The results indicate that the location of the largest collapse in the tube occurs close to the downstream end. In the oscillating regime, sections of the tube downstream of the largest mean collapse experience the largest oscillations in the entire tube that are completely coherent and in phase. At a certain streamwise distance upstream of the largest collapse, a switch in the direction of oscillations occurs with respect to those downstream. Physically, when the tube experiences constriction downstream of the location of the largest mean collapse, this causes the accumulation of fluid and build-up of pressure in the upstream regions and an expansion of these sections. Fluctuations in the downstream flow field are significantly influenced by tube fluctuations along the minor axes. The fluctuations in the downstream flowfield are influenced by the propagation of disturbances due to oscillations in tube geometry, through the advection of fluid through the tube. Further, the manifestation of the LU-type pressure fluctuations is found to be due to the variation in the propagation speed of the disturbances during the different stages within a period of oscillation of the tube.more » « less
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null (Ed.)Breathing biomarkers, such as breathing rate, fractional inspiratory time, and inhalation-exhalation ratio, are vital for monitoring the user's health and well-being. Accurate estimation of such biomarkers requires breathing phase detection, i.e., inhalation and exhalation. However, traditional breathing phase monitoring relies on uncomfortable equipment, e.g., chestbands. Smartphone acoustic sensors have shown promising results for passive breathing monitoring during sleep or guided breathing. However, detecting breathing phases using acoustic data can be challenging for various reasons. One of the major obstacles is the complexity of annotating breathing sounds due to inaudible parts in regular breathing and background noises. This paper assesses the potential of using smartphone acoustic sensors for passive unguided breathing phase monitoring in a natural environment. We address the annotation challenges by developing a novel variant of the teacher-student training method for transferring knowledge from an inertial sensor to an acoustic sensor, eliminating the need for manual breathing sound annotation by fusing signal processing with deep learning techniques. We train and evaluate our model on the breathing data collected from 131 subjects, including healthy individuals and respiratory patients. Experimental results show that our model can detect breathing phases with 77.33% accuracy using acoustic sensors. We further present an example use-case of breathing phase-detection by first estimating the biomarkers from the estimated breathing phases and then using these biomarkers for pulmonary patient detection. Using the detected breathing phases, we can estimate fractional inspiratory time with 92.08% accuracy, the inhalation-exhalation ratio with 86.76% accuracy, and the breathing rate with 91.74% accuracy. Moreover, we can distinguish respiratory patients from healthy individuals with up to 76% accuracy. This paper is the first to show the feasibility of detecting regular breathing phases towards passively monitoring respiratory health and well-being using acoustic data captured by a smartphone.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/admt.202300627
