Flow Rate Sensor Research Articles

Unsteady flow rate in transient, incompressible pipe flow

AbstractKnowledge regarding current flow rates through pipes and other components is crucial for most hydraulic systems. The hydraulic power can be computed in combination with the pressure, which may be used in many applications, such as predictive maintenance. Most flow rate sensors used in the field of fluid power operate invasively. Therefore, the measurement process itself alters the flow rate. Furthermore, most sensors operate accurately for stationary flow but produce inaccurate measurements for transient flow. A well‐known method of determining the flow rate is to measure the pressure difference between two points along a pipeline and calculate the flow rate based on the law of Hagen and Poiseuille. However, since the relation mentioned above only applies to laminar, steady, and incompressible flow, its usefulness for transient flows is limited. This paper derives a system equation based on the fundamental laws of fluid mechanics, which describe transient, incompressible pipe flow. As a result, the fundamental equations are solved in the Laplace domain and subsequently transformed back into the time domain. The four‐pole theorem relates the pressure difference and the volumetric flow rate. The analytical solution consists of a convolution integral containing a weighting function and the pressure difference. Compared to a simulation, the novel equation displays high accuracy for transient and stationary incompressible pipe flow. This equation paves the way for a soft sensor, which allows the noninvasive measurement of arbitrary volumetric flow rates within pipes.

An early warning method of pipeline leakage monitoring with limited leakage samples

Aiming at the problems of low computational efficiency of the real-time transient model (RTTM), and training difficulty of data-driven models with small leakage samples in existing pipeline leakage monitoring methods, this paper proposes a novel pipeline leakage detection model (Multiple Regression Modeling of Real-time Transients, MR-RTTM) based on the fusion of real-time transient mechanism model and data-driven inference. Firstly, the real-time transient mechanism model of pipelines is derived based on the Bernoulli equation, and the required eigenvalues and label values of the data-driven model are determined to achieve the integration of the mechanism and the data-driven model. Secondly, MR-RTTM is constructed by applying the data of pipelines in normal working conditions and combining with the multiple regression algorithm to fit the real-time transient mechanism model of pipelines. An optimal threshold interval selection method of the model is also proposed based on the probability of detection (POD) and the probability of false call (POFC). Experiments show that MR-RTTM can detect pipeline leakage by arranging common pressure and flow rate sensors in the pipeline inlets and outlets, and the minimum detectable leakage is 1 % with an acceptable interval of 80 %∼100 % for POD and 0 ∼ 10 % for POFC, and the leakage discrimination rate is up to 100 %.

Assessment of sealing systems impact on the vibration and environmental safety of rotary machines

Energy saving control algorithms of centrifugal fans/pumps are based on the use of the frequency-controlled induction motor drives and pressure or flow rate sensors, the costs of which are comparable to the cost of the fans/pumps for low-power applications. The paper develops a new and simple estimation approach of the pressure and flow rate, utilising the measured Root Mean Square (RMS) value of the stator current, estimated motor’s input active power, reference stator voltage frequency and feed-forward backpropagation artificial neural network. The error percentage for both flow rate and pressure in experimental and estimated data is within the range of ±5%, which conforms to the ISO 13348 standard. A test rig for the rapid control prototyping of the fan is designed, and necessary design and test procedures are developed. The estimation approach is verified experimentally and demonstrates better estimation accuracy compared to the existing and possible similar simple approaches. The developed algorithm can be easily embedded into the industrial variable frequency drives without any hardware changes.

Improved Flow Rate and Pressure ANN Estimators for a Centrifugal Fan with Induction Motor Drive

Energy saving control algorithms of centrifugal fans/pumps are based on the use of the frequency-controlled induction motor drives and pressure or flow rate sensors, the costs of which are comparable to the cost of the fans/pumps for low-power applications. The paper develops a new and simple estimation approach of the pressure and flow rate, utilising the measured Root Mean Square (RMS) value of the stator current, estimated motor’s input active power, reference stator voltage frequency and feed-forward backpropagation artificial neural network. The error percentage for both flow rate and pressure in experimental and estimated data is within the range of ±5%, which conforms to the ISO 13348 standard. A test rig for the rapid control prototyping of the fan is designed, and necessary design and test procedures are developed. The estimation approach is verified experimentally and demonstrates better estimation accuracy compared to the existing and possible similar simple approaches. The developed algorithm can be easily embedded into the industrial variable frequency drives without any hardware changes.

Flow Rate Sensor inside Infusion Tube

Infusion systems are widely used in clinical medicine. Intravenous infusion therapy must be monitored to ensure patient safety. We proposed a compact flow rate sensor device based on the time-of-flight method. This device included one ceramic heater and two infrared sensors. Practical sensor prototypes were fabricated and characterized. The response time was 30 s. The sensor range was estimated to be 33 dB from μL/min to tens of mL/min, covering almost the entire usage range This flow rate sensor can be applied to common infusion tubes. Through the use of a mobile phone app, detailed information can be presented in real time.

Enhancement of bionic cilia flow rate sensor signals by single-well stochastic resonance

Abstract Based on the characteristics of non-periodic signals in bionic cilia flow rate sensors, an investigation on the real-time signal processing methodologies is conducted in single-well stochastic resonance. In this research, we derive a model for an adaptive single-well stochastic resonance system featuring nonlinear recuperation. To assess the scientific robustness and practical viability of the algorithm, a validation experiment was formulated utilizing the single-well stochastic resonance capacitance online detection and processing hardware system. The experimental findings show a notable reduction in noise interference, a marked enhancement in signal quality, and an approximate 0.55 increase in the maximum cross-correlation coefficient among sensor signals. Consequently, the model fulfills the requirements for effectively handling non-periodic signals from sensors.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Development of a Real-time Force-based Algorithm for Infusion Failure Detection.

Continuous subcutaneous insulin infusion (CSII) is a common treatment option for people with diabetes (PWD), but insulin infusion failures pose a significant challenge, leading to hyperglycemia, diabetes burnout, and increased hospitalizations. Current CSII pumps' occlusion alarm systems are limited in detecting infusion failures; therefore, a more effective detection method is needed. We conducted five preclinical animal studies to collect data on infusion failures, utilizing both insulin and non-insulin boluses. Data were captured using in-line pressure and flow rate sensors, with additional force data from CSII pumps' onboard sensors in one study. A novel classifier model was developed using this dataset, aimed at detecting different types of infusion failures through direct utilization of force sensor data. Performance was compared against various occlusion alarm thresholds from commercially available CSII pumps. The testing dataset included 251 boluses. The Bagging classifier model showed the highest performance metrics among the models tested, exhibiting high accuracy (96%), sensitivity (94%), and specificity (98%), with lower false-positive and false-negative rate compared with traditional occlusion alarm pressure thresholds. Our study developed a novel non-threshold classifier that outperforms current occlusion alarm systems in CSII pumps in detecting infusion failures. This advancement has the potential to reduce the risk of hyperglycemia and hospitalizations due to undetected infusion failures, offering a more reliable and effective CSII therapy for PWD. Further studies involving human participants are recommended to validate these findings and assess the classifier's performance in a real-world setting.

Self-Powered Flow Rate Sensing via a Single-Electrode Flowing Liquid Based Triboelectric Nanogenerator.

Recently, triboelectric nanogenerators (TENGs) have emerged as having an important role in the next wave of technology due to their large potential applications in energy harvesting and smart sensing. Recognizing this, a device based on TENGs, which can solve some of the problems in the liquid flow measurement process, was considered. In this paper, a new method to measure the liquid flow rate through a pipe which is based on the triboelectric effect is reported. A single-electrode flowing liquid-based TENG (FL-TENG) was developed, comprising a silicon pipe and an electrode coated with a polyvinylidene fluoride (PVDF) membrane. The measured electrical responses show that the FL-TENG can generate a peak open-circuit voltage and peak short-circuit current of 2.6 V and 0.3 µA when DI water is passed through an 8 mm cell FL-TENG at a flow rate of 130 mL/min and reach their maximum values of 17.8 V-1.57 µA at a flow rate of 1170 mL/min, respectively. Importantly, the FL-TENG demonstrates a robust linear correlation between its electrical output and the flow rate, with the correlation coefficient R2 ranging from 0.943 to 0.996. Additionally, this study explores the potential of the FL-TENG to serve as a self-powered sensor power supply in future applications, emphasizing its adaptability as both a flow rate sensor and an energy harvesting device.

Wearable microfluidic sweat collection platform with a calorimetric flow rate sensor for realtime and long-term sweat rate measurements

This work presents the design and fabrication of a wearable microfluidic patch-based system for sweat collection with a calorimetric flow rate sensor based on heat convection for measuring sweat rate (SR). The effects were predicted using a 3D multi-physics simulator and were verified on a fabricated patch made of polyimide layers. The sensor can detect surface temperature gradients of 302–312 K caused by fluid flowing thru the microfluidic channels at a rate of 0.5–23 µg s−1 that fall within the physiological range of SR. Meanwhile, the relation between flow rate and temperature gradient is highly linear (Pearson r2 = 0.999) and repeatable. This work also demonstrates a low-cost method for patterning microfluidic channels on flexible substrates which can be used for mass production of wearable patches.

Flowing Liquid-Based Triboelectric Nanogenerator Performance Enhancement with Functionalized Polyvinylidene Fluoride Membrane for Self-Powered Pulsating Flow Sensing Application.

Pulsating flow, a common term in industrial and medical contexts, necessitates precise water flow measurement for evaluating hydrodynamic system performance. Addressing challenges in measurement technologies, particularly for pulsating flow, we propose a flowing liquid-based triboelectric nanogenerator (FL-TENG). To generate sufficient energy for a self-powered device, we employed a fluorinated functionalized technique on a polyvinylidene fluoride (PVDF) membrane to enhance the performance of FL-TENG. The results attained a maximum instantaneous power density of 50.6 µW/cm2, and the energy output proved adequate to illuminate 10 white LEDs. Regression analysis depicting the dependence of the output electrical signals on water flow revealed a strong linear relationship between the voltage and flow rate with high sensitivity. A high correlation coefficient R2 within the range from 0.951 to 0.998 indicates precise measurement accuracy for the proposed FL-TENG. Furthermore, the measured time interval between two voltage peaks precisely corresponds to the period of pulsating flow, demonstrating that the output voltage can effectively sense pulsating flow based on voltage and the time interval between two voltage peaks. This work highlights the utility of FL-TENG as a self-powered pulsating flow rate sensor.

Flow rate sensor based on high-order whispering gallery mode in optofluidic microcavity

Flow rate sensor based on high-order whispering gallery mode in optofluidic microcavity

Real-time embedded pressure and flow rate sensor in polydimethylsiloxane-based microchannel using fiber Bragg grating technique

Polydimethylsiloxane (PDMS) has attracted huge interest as the soft material for microchannel fabrication, due to its high biocompatibility, durability and low cost. The rheological properties of the fluid and the deformable properties of PDMS substrate have imposed significant impacts on the fluid flow in the microchannel, therefore it is of critically importance to develop sensing methods for the accurate flow rate monitoring and the flow-induced pressure measurement without disturbing the fluid flow in the microchannel. This paper presents an embedded and miniaturized pressure sensor based on the Fiber Bragg Grating (FBG) technique for the measurement of both flow rate and flow-induced pressure in PDMS-based microchannel. This method has demonstrated a pressure sensitivity of 0.06 p.m./Pa and the Limit of Detection (LoD) for pressure is 19.5 Pa. The linearity between flow-induced pressure and flow rate in PDMS-based microchannel is about 4.86 Pa(ml/h)−1 based on simulation results and is within the range between 4.33 Pa(ml/h)−1 and 6.67 Pa(ml/h)−1 obtained from experimental results. The LoD of flow rate using this method is within the range between 2.9 ml/h and 4.5 ml/h. The random instability induced by the operation of the reconnection of syringe back to the liquid flow system was identified and the calibration of this instability is also presented in this paper. Using this method, the pressure and flow rate within PDMS-based microchannel can be monitored with high reliability and high sensitivity, and this will benefit the application of PDMS-based microfluidics in areas that require highly precise control in flow conditions, e.g. biosensing, cell culture, and drug delivery.

Portable Intelligent Electromagnetic Flowmeter Controlled by Magnetic Induction Intensity

In this paper, we propose a portable intrinsically safe, intelligent electromagnetic flowmeter for the application of coal mines and provide a detailed description of its working principle. In terms of hardware circuit design, several important modules, including electromagnetic flow rate sensor, excitation circuit, core control circuit, signal conditioning circuit, and air traffic control detection circuit, were analyzed and designed in sequence. It adopts a high-performance, low-power MCU STM32407ZET6 and high-precision chips, ensuring measurement accuracy and reducing power consumption. The excitation method using low-frequency rectangular waves dynamically adjusts the sampling interval time, further prolonging the battery life. Finally, the performance of the electromagnetic flowmeter was measured, and the experimental data showed that the measured flow rate was basically linear with the output voltage. The relative indication error of the electromagnetic flowmeter was within 0.3%, and the repeatability fluctuated within the range of 0.03%. The flowmeter has good measurement accuracy, which is suitable for long-term use in coal mining environments.

An IoT-Based Water Leakage Detection and Localization System

In this research, we present a proposed Internet of Things based model, which we have named iWaLDeL, for the detection and localization of water leakages which we simulated for the Ghana Water Company (GWC) pipe network, using YF-S201 Flow Rate Sensors and Static Leak Detection techniques. Through an extensive review of existing methods, the research highlights the limitations of traditional detection approaches and emphasizes the potential of modern technologies. The iWaLDeL system harnesses the power of IoT devices, deploying an array of strategically placed sensors capable of detecting leakages and pressure variations. These sensors form a distributed network which communicates with a central hub, ensuring comprehensive coverage of the monitored area. Crucially, the research delves into the innovative aspect of leak localization. By combining data from multiple sensors and pipes, the system can estimate the precise location of a leakage following a mathematical model we developed. This localization capability significantly reduces the time required for maintenance teams to address leakage issues, minimizing water loss and further damages. To validate its effectiveness, the system was prototyped and tested, which demonstrated a successful leak detection and localization, showcasing its adaptability in both residential and commercial settings. The system’s seamless integration with existing smart infrastructure enhances its feasibility for real-world implementation.

Automation System Architecture of pH Neutralization Process in Batik Wastewater Treatment Plant

Batik production in home industries needs support wastewater treatment in a limited space. Therefore, a modular-type, lab-scale wastewater treatment plant (WWTP) with a capacity of 87.75 liters and a dimension of 1.5 m × 0.75 m × 1 m has been constructed. The WWTP combines three significant processes: equalization, neutralization, and adsorption. This study focuses on the monitoring systems for the pH neutralization process. The system uses four microprocessors in a Master/Slave architecture and the I2C protocols for data communication between processors. The measuring instruments include pH, temperature, turbidity, and flow rate sensors. The actuators consist of submersible and peristaltic pumps. The operating range of pH, temperature, turbidity, and flow rate sensors are 0–14, 25–81.44℃, 0–3000 NTU, and 6.49–20.84 ml/s respectively. The pumps work in Pulse Width Modulation (PWM) mode with an operating range of 125–225 PWM. All sensors and actuators operate linearly within the operating range with a correlation coefficient of 0.97–1.

Design Analysis of Portable 1 Channel Infusion Device Analyzer Using Sensor SKU 237545

An infusion pump is a tool used to inject a certain amount of fluid into the patient's body through the patient's veins continuously over a certain period of time. A syringe pump is a tool that functions to push the syringe rod so that it can produce a flow ranging from microliters to milliliters per minute periodically with high accuracy. Very often there are problems with blockages or occlusion when using infusion pumps and syringe pumps. The occlusion limit set is ≤20 PSI according to ECRI. The presence of occlusion in the infusion pump and syringe pump can be identified when there is an alarm buzzer which will sound when a blockage is detected. A 1 Channel Portable Infusion Device Analyzer has been designed using the SKU 237545 Sensor, namely by using a 1 channel flowrate and occlusion sensor and making it portable to be efficient. For this reason, it is necessary to analyze the performance of the tools that have been created. How accurate is it? From the results of performance testing, Oclusion was corrected at 0.242 psi and 0.3 Psi. For flow rate, the largest correction was 2.4 ml/hour and the uncertainty was 6,046 ml/hour. This shows that the accuracy of the design is still quite high and the resulting tool is still not stable, this can be seen from the uncertainty value. The uncertainty that occurs is likely due to the sensitivity of the droplet sensor related to the detection time of the droplet

Numerical and experimental investigation of static shaft Wankel expander for compressed-air energy storage

Compressed air energy storage (CAES) is a promising technology for storing mechanical and electrical energy using the gas power cycle. The expansion device is a critical component of the CAES that determines the overall performance of the system. Standard Wankel expander (SWE) is one of the volumetric expanders which has several advantages including low vibration, ability to produce high power output, low manufacturing cost and less moving parts. However, SWE requires valves for timing the inlet and outlet flow and a balancing system to ensure reliable operation. Static shaft Wankel expander (SSWE) is an attractive solution to enable valves’ removal and the need for balancing system. This paper presents a detailed experimental and numerical investigation of an SSWE performance at various operating pressures and temperatures for CAES application. An advanced computational fluid dynamic simulation model taking into account the dynamic motion of the SSWE and utilising real gas air properties. A compressed air test rig was constructed and instrumented with temperature, flow rate, pressure and torque sensors. Experimental testing at temperatures 20 °C to 80 °C and pressures of 1.5 bara to 3 bara was conducted and compared to the CFD simulations results. Correlations were developed for the friction power loss. Experimental results showed that the developed SSWE can produce power output of 504 W at 80 °C and 3 bara and its isentropic efficiency reached 71 % at 60 °C and 2 bara.

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

A LoRaWAN-based IoT System for Leakage Detection in Pipelines

Leakages in a pipeline are an important problem due to the potential economic and environmental hazard they present. In this study, we proposed a LoRaWAN-based approach for detecting and localizing leakages in pipelines. Our study includes an experimental setup that simulates a pipeline network with pressure and flow rate sensors attached. The flow rate and pressure data were transmitted through LoRaWAN to a receiver, which in turn uploads the data to a cloud server using a cellular network. The receiver compares the flow rate reading from all the monitoring nodes attached to the pipeline network. If flow rate reading from successive nodes presents a percentage variation of more than 1.5%, a leak is confirmed to have taken place. The flow rate readings can also be used to localize the leak. The resolution of the leak detection is dependent on the number of monitoring nodes on the pipeline network. In our study, the pressure readings were found to be insufficient to provide reliable evidence of leakages. In our specific situation, due to the relatively short length of the experimental pipeline network, a pressure drop of up to 38.2% was recorded between successive nodes with an overall pressure loss of 62%, making pressure data unsuitable for leak detection in the short pipeline network.