Flow Sensor Research Articles

A PDMS-Based Flexible Calorimetric Flow Sensor With Double-Bridge Technology

A PDMS-Based Flexible Calorimetric Flow Sensor With Double-Bridge Technology

Performance of a MEMS Flow Sensor With High Temperature

Performance of a MEMS Flow Sensor With High Temperature

ANALYSIS OF THE CHARACTERISTICS OF AUTOMOTIVE MASS AIR FLOW SENSORS

An analysis of the characteristics of automotive mass air flow sensors is made. A test system has been developed to study the sensors. The measurement data is processed with MATLAB using curve fitting.

Flow sensors for on-chip microfluidics: promise and challenges

Flow sensors for on-chip microfluidics: promise and challenges

Vehicle flow indication and identification using FBG sensors

Abstract This paper proposes a vehicle flow monitoring system utilizing fiber Bragg grating (FBG) sensor technology. The system comprises a prototype road model along with a car with varying loads. The FBG sensor is embedded in the road surface for sensing the strain induced by the vehicle movement. The laboratory experiments were conducted using an embankment model equipped with FBG sensor to identify vehicle movement under static and movable loads. Under static condition, different loads were applied on the car and the corresponding wavelength shifts have been measured using FBG-interrogator. Under dynamic condition, the vehicle was moved with certain speed and the corresponding wavelength shifts were recorded over time. The sensitivity of FBG for vehicle load sensing has been obtained as 0.3 pm/N. The proposed vehicle flow sensor system offers good accuracy and sensitivity and has potential applications for real-time vehicle flow monitoring. The proposed sensor system can be used to analyze road surface irregularities, crucial for assessing heavy-vehicle fatigue.

Flow Profiles Identify Sources of Poor Metered Dose Inhaler Technique: An observational study in long-term pMDI users

BackgroundInhaled medications are commonly used as therapy for obstructive lung disease (OLD) yet poor technique from pressurized metered dose inhalers (pMDI) can severely limit drug delivery and therapeutic efficacy. A novel flow sensor has been developed to characterize inhalation flow profiles from pMDIs. Research QuestionAmong OLD subjects, flow profiles during pMDI inhalation maneuvers would expose poor patterns of pMDI use, which can be used to remediate pMDI technique. Study Design and MethodsA novel flow sensor was coupled to a placebo pMDI inhaler to characterize inhalation technique in 70 OLD participants from a pulmonary clinic. pMDI inhalation flow profiles generated actuation timing, mean inspiratory airflow (MIF) and inspired volume before and after visualizing these features. McNemar’s was used to characterize the impact of training on pMDI inhalation metrics. The post-actuation inspired volume was normalized to inspiratory capacity (IC). ResultsAmong participants with 17.4 (SD = 17.9) years of pMDI use, flow profiles uncovered mistimed actuations in 47.1% and poor inspiratory flow rates in 30.0% of subjects. After visualizing flow profiles, participants improved actuation timing (c2(1) =12.042, p < .001), mean inhaled volume (87.9 to 105.6% of IC, p<0.001) and the combined inhalation metrics (c2(1) =8.45, p = 0.003). InterpretationFlow profiles helped uncover and remediate specific defects in pMDI technique in the majority of chronic OLD users. Flow profiles from inhaled medications can be used to enhance drug delivery to the lung.

Small-volume omnidirectional intelligent airflow sensor with differential pressure measurement

Small-volume omnidirectional intelligent airflow sensor with differential pressure measurement

Performance of five different flow sensors for equine spirometry: a bench top study using clinical monitors

Performance of five different flow sensors for equine spirometry: a bench top study using clinical monitors

К МЕТОДИКЕ ФОРМИРОВАНИЯ ЦИФРОВОГО ДВОЙНИКА ТЕХНОЛОГИЧЕСКОГО ПРОЦЕССА СУШКИ ОРГАНИЧЕСКИХ СЕМЯН ТИМОФЕЕВКИ

An important specific feature of the functioning of machines and equipment in the technologies of post-harvest processing of grain and seeds is the probabilistic variability of external conditions that negatively affect the efficiency of their work. The drying process is also characterized by non-stationarity in its probabilistic and statistical characteristics. The methods of forming digital twins of stationary processes are quite well mastered, but the construction of digital twins of non-stationary processes is a much more complex task, the solution of which requires taking into account the nature of non-stationarity, as well as the features and internal structure of the processes being modeled. The purpose of the research is to build a digital twin of the non-stationary process of drying timothy seeds and assess the reliability of the resulting model. The experimental data were obtained by drying Leningradskaya 204 timothy grass seeds from the field organic crop rotation using a universal drying unit with a Fauna PMDR4 digital seed moisture meter equipped with submersible flow sensors. As a result of the experimental studies, realizations of random processes of change in seed moisture content at the final stage of drying were obtained for constructing mathematical models using statistical identification methods. Discrete analogs of the processes under study were constructed using experimental correlation functions approximated by the corresponding functions to obtain the parameters of the modeling recurrent equations. The considered algorithm for modeling the dryer operation allows reproducing processes of unlimited length and modeling various conditions and operating modes to find the specified optima for quality, energy consumption and environmental friendliness. The accuracy of the simulation results was determined based on the convergence of the moment functions of the experimental and modeled technological processes. Estimates of statistical characteristics of the simulated process of drying timothy grass seeds with 95% probability correspond to statistical estimates of the drying process obtained in the course of experimental studies, and the graphs of their normalized correlation functions clearly confirm the identity of the processes. The obtained mathematical dependencies of the technological process of drying organic timothy grass seeds are its digital twin, allowing the methods of simulation modeling to solve optimization problems.

Bioinspired Photoluminescent "Spider Web" as Ultrafast and Ultrasensitive Airflow-Acoustic Bimodal Sensor for Human-Computer Interaction and Intelligent Recognition.

Nature provides massive biomimetic design inspiration for constructing structural materials with desired performances. Spider webs can perceive vibrations generated by airflow and acoustic waves from prey and transfer the corresponding information to spiders. Herein, by mimicking the perception capability and structure of a spider web, an ultrafast and ultrasensitive airflow-acoustic bimodal sensor (HOF-TCPB@SF) is developed based on the postfunctionalization of hydrogen-bonded organic framework (HOF-TCPB) on silk film (SF) through hydrogen bonds. The "spider web-like" HOF-TCPB@SF possesses light weight and high elasticity, endowing this airflow sensor with superior properties including an ultralow detection limit (DL, 0.0076 m s-1), and excellent repeatability (480 cycles). As an acoustic sensor, HOF-TCPB@SF exhibits ultrahigh sensitivity (105140.77 cps Pa-1 cm-2) and ultralow DL (0.2980 dB), with the greatest response frequency of 375 Hz and the ability to identify multiple sounds. Moreover, both airflow and acoustic sensing processes show an ultrafast response speed (40 ms) and multiangle recognition response (0-180°). The perception mechanisms of airflow and acoustic stimuli are analyzed through finite element simulation. This bimodal sensor also achieves real-time airflow monitoring, speech recognition, and airflow-acoustic interoperability based on human-computer interaction, which holds great promise for applications in health care, tunnel engineering, weather forecasting, and intelligent textiles.

Horti-IoT: a new data framework for Dutch horticulture

Abstract Consistency issues limit the sharing of horticultural data across multiple systems, resulting in challenges for users to analyze data effectively across various systems utilizing artificial intelligence technology. Introducing data governance principles can help standardize and unify data practices, making it easier for analysts to locate, comprehend, transfer and integrate data from diverse sources to enable data-driven horticulture. Implementing data governance and principles specific to horticulture can assist in standardizing the layout and format of data structures from different sources. This study aims to propose a new governance framework, Horti-IoT, based on the Data Management Body of Knowledge and several structured frameworks for the Internet of Things (IoT) governance that will lead to data-driven horticulture. This study is empirical in nature. The Dutch horticulture stakeholders are involved in this initiative, providing the data, knowledge, and experiences needed for this study. The data stream from various sources, including camera images, sap flow sensors, climate sensors and manually measured growth data. The key findings following the implementation of the Horti-IoT framework’s principles are reduced workload for data analysts, efficiency in plant monitoring, savings time in pre-processing, enhanced water resource management, reduced system administrator contacts and compliance with General Data Privacy Regulation. The new proposed Horti-IoT framework, compatible with Dutch horticulture, is presented. The data were obtained from the Lab greenhouse at the World Horti Centre in the Netherlands, in the framework of the Regionale SIA RAAK MKB call March 2022-September 2024 subsidy funds for project title ‘Gewasgroei Goed Gemeten (GeGoGe). This project is a collaboration between three educational institutions. Inholland University of Applied Science, the Hague University of Applied Science, Lentiz Vocational School, and stakeholders.

Anesthetic Gas Scavenging System for Gas Evacuation in the ICU.

Inhaled sedation is increasing in ICUs, with active carbon filters (ACFs) commonly used for evacuating halogenated gases. However, the potential benefits of a waste anesthetic gas system (WAGS) similar to the ones used in operating rooms should be explored. To limit the suction over the flow sensor where the WAGS is connected on ICU ventilators, an anesthetic gas receiving system (AGRS) is required, constituting with the WAGS an active gas receiving and scavenging system (AGRSS). Ensuring that this whole device does not compromise the flow sensor reliability is crucial. The aim of this study was to compare various gas evacuation devices and assess the reliability of AGRSS on ICU ventilators. In this experimental study, pressures and flows were recorded during the ventilation of a test lung using various ventilator settings and gas evacuation methods: no device (reference condition), ACF, the WAGS alone, AGRSS (WAGS and AGRS together), and the expiratory valve connected to the medical vacuum system with the AGRS in between. Visual comparisons of the pressure and flow curves followed by a statistical analysis comparing median pressures and flows of each device to the reference were performed. The test lung model demonstrated consistent comparability in pressures and flows among all devices, except for the WAGS alone, which exhibited discordance through significant overestimation or underestimation. These findings indicate that using a WAGS with the AGRS system appeared to be reliable for managing gas evacuation in ICUs without compromising pressure or flow delivery. The data from this experimental trial should be confirmed with clinical studies involving human subjects. Given the increasing use of inhaled sedation in ICUs, these results support the daily application of the WAGS with the AGRS for gas evacuation, similar to its established use in anesthesiology.

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

Machine learning method monitors non-Newtonian fluid flow in real time

Combining a contactless flow sensor with a neural-network algorithm allows accurate measurement and control of non-Newtonian fluids for healthcare and manufacturing applications.

Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-By-Feel: Bioinspired Approach and Application.

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45° sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

Computer-based monitoring system of retort processes for energy efficiency and carbon emission reduction

Retort sterilization is critical in the food industry. However, retorts are not smart enough to fully monitor and calculate their energy usage to reduce their carbon emissions. In this project, sensors and network equipment were installed in a retort, and a PC-based system was used to process the collected data. To monitor the sterilization status and energy consumption, we added flow sensors and electricity meters and built a visual identification system. Carbon emissions were calculated, and energy conservation and carbon emission reduction strategies were explored. According to the monitoring data, the electricity consumption of the air compressor, boiler, and natural gas combustion accounted for 80 % of the total carbon emissions. If the equipment cannot be changed, the cooling water system can be stopped in noncooling stages to conserve energy. This strategy reduced the energy consumption of the cooling system by 70 % and was the most effective strategy for this project.

Air Leakages at Microvalves: Pressure Decay Measurements and Extended Continuum Modelling of Knudsen Flows

To improve the performance of valves in relation to the leakage rate, a comprehensive evaluation of the valve characteristics and behavior during pressure exposure is important. Often, these low gas flow rates below 0.1 cm3/min cannot be accurately measured with conventional flow sensors. This paper presents a small and low-cost test rig for measuring gas leakage rates accurately, even far below 0.1 cm3/min, with the pressure decay method. These leakage flows are substantiated with a flow model, where we demonstrate the feasibility of modeling those gas flows with an extended Navier–Stokes framework to obtain more accurate theoretical predictions. As expected, the comparison to the experimental results proves that the classical Navier–Stokes system is unsuitable for modeling Knudsen flows. Hence, self-diffusion of gas, a wall-slip boundary condition, and an effective mean free path model were introduced in a physically evident manner. In terms of the calculated mass flow, while self-diffusion and slip boundary conditions explain deviations from the classical Navier–Stokes equation for Knudsen numbers already smaller than 1, the effective mean free path model has an effect, especially when Kn &gt; 1. For simplified conditions, an analytical solution was presented and compared to the results of an OpenFOAM CFD-solver for flow rates through more complex gap-flow geometries of the flap valve. Hereby, acceptable deviations between 10% and 20% were observed. A comparison with measurement results was carried out. The reproducibility of the measurement method was verified by comparing multiple measurements of one silicon microvalve sample to a state-of-the-art flow sensor. Three geometrically similar passive silicon microvalves were measured with air overpressure decreasing from 15 kPa relative to atmospheric pressure. Maximum gas volume flowing in a blocking direction of 1–26 µL/min with high reproducibility and marginal noise were observed.

Design and Optimization of Graphene Film Micro Flow Sensor Based on Uncertainty Analysis

Design and Optimization of Graphene Film Micro Flow Sensor Based on Uncertainty Analysis

Neural network-enabled, all-electronic control of non-Newtonian fluid flow.

Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 μl min-1) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups.

Influence of compliance and resistance of the test lung on the accuracy of the tidal volume delivered by the ventilator

BackgroundLarge variations in respiratory system compliance and resistance may cause the accuracy of tidal volume (VT) delivery beyond the declared range. This study aimed at evaluating the accuracy of VT delivery using a test lung model to simulate pulmonary mechanics under normal or disease conditions.MethodsIn vitro assessment of the VT delivery accuracy was carried out on two commercial ventilators. Measurements of the inspired and expired VT from the ventilator and FlowAnalyser were compared to evaluate the separated and combined influences of compliance and resistance on the delivered VT accuracy. To do this, the errors of five delivered volumes (30 ml, 50 ml, 100 ml, 300 ml, and 500 ml) were checked under 29 test conditions involving a total of 27 combinations of resistance and compliance.ResultsFor the tested ventilator S1 with a flow sensor near the expiratory valve, the average of expired VT errors (ΔVTexp) in three measurements (4 test conditions for each measurement) correlated to test lung compliance (r=-0.96, p = 0.044), and the average of inspired VT errors (ΔVTins) correlated to compliance (r = 0.89, p = 0.106); for the tested ventilator S2 with a flow sensor located at the Y piece, no clear relationship between compliance and ΔVTexp or ΔVTins was found. Furthermore, on two ventilators tested, the current measurements revealed a poor correlation between test lung resistance and ΔVTins or ΔVTexp, and the maximum values of ΔVTexp and ΔVTins correspond to the maximum resistance of 200 cmH2O/(L/s), at which the phenomenon of the flap fluttering in the variable orifice flow senor was observed, and the recorded peak inspiratory pressure (Ppeak) was much higher than the Ppeak estimated by the classical equation of motion. In contrast, at the lower resistance values of 5, 20, 50 and 100 cmH2O/(L/s), the recorded Ppeak was very close to the estimated Ppeak. Overall, the delivered VT errors were in the range of ± 14% on two ventilators studied.ConclusionsDepending on the placement site of the flow sensor in the ventilator circuit, the compliance and resistance of the test lung have different influences on the accuracy of VT delivery, which is further attributed to different fluid dynamics effects of the compliance and resistance. The main influence of compliance is to raise the peak inspiratory pressure Ppeak, thereby increasing the compression volume within the ventilator circuit; whereas a high resistance not only contributes to elevating Ppeak, but more importantly, it governs the gas flow conditions. Ppeak is a critical predictive indicator for the accuracy of the VT delivered by a ventilator.