Flow Measurement Method Research Articles

Low-Cost Device for Measuring Wastewater Flow Rate in Open Channels

This research paper describes the development of a low-cost device for measuring wastewater flow rates in open channels, a significant advancement enabled by the evolution of microcomputers and processing techniques. A laboratory setup was constructed to validate the device’s accuracy against a standard flow measurement method, optimizing key parameters to achieve a linear relationship between detected and set flow rates, while considering hardware limitations and energy efficiency. The central focus of the research was developing a method to measure the velocity of contaminated fluid using ultrasonic signals, employing the cross-correlation method for signal delay analysis in a stochastic environment. This was complemented by a procedure to measure fluid levels, also based on ultrasonic signals. The device’s reliability was assessed through repeatability and uncertainty measurements, confirming its accuracy with an extended uncertainty not exceeding an average of 3.47% for flows above 40 L/min. The device has potential to provide valuable data on the operational dynamics of sanitary networks, crucial for developing and calibrating simulation models.

Analysis and numerical simulation of different flowmeters based on an open channel in an irrigation area

ABSTRACT This study establishes an ultrasonic open channel flow measurement test and numerical simulation model and systematically compares the performance and reliability of the ultrasonic method and the traditional flowmeter in flow measurement in the irrigation area by observing and analysing the test data and combining it with the numerical simulation. In the indoor ultrasonic flowmeter flow measurement experiments in the trapezoidal nullah section, the flow rate measurement was carried out by using a high-precision variable slope flume and a variety of flow measurement tools. A numerical study was carried out using the VOF method to calculate the flow in the open channel flume under two-phase flow conditions. Combining experimental measurements and numerical simulations, this study aims to quantify the magnitude of flow measurement errors and water surface fluctuations in open channel flumes and to address the discrepancies between different flow measurement methods in open channels in irrigation areas.

Development of a Novel High Temperature Continuous Particle Mass Flow Measurement Device for Particle-Based CSP Applications

Particle-based concentrating solar power (CSP) technology is one of the generation three (Gen3) CSP technologies that has potential for lowering the levelized cost of electricity produced by this solar thermal power systems. Commercially available ceramic particles, which are rated for high temperature applications up to 1200 °C, were selected for this study. This medium is commonly used as an energy carrier or heat transfer medium in Gen3 CSP research and development efforts [1]. Prior studies at the National Solar Thermal Test Facility (NSTTF) have investigated and evaluated various mass flow measurement methods, and a simple gravimetric temporal load measurement method was chosen as the best candidate for the research purpose [2]. This mass flow measurement method is a batch process and is not a viable solution for commercial-scale power generation applications based on cost, space, process control, and practical system integration factors. In-line and continuous particle mass flow measurement will play an integral role in efficient and cost effective Gen3 CSP particle technologies. Process parameters, including energy absorbed by the particles, receiver efficiency, temperature dependent flow phenomena, and general high temperature measurement reliability are all rely on repeatable and accurate measurement of particle mass flow under various operating conditions. Ceramic particles, like those used in this application, are not conventionally used as an energy carrier. A novel concept for continuous particle mass flow at a wide range of operating temperatures is currently under development and planned to be tested at the NSTTF, called the Particle In-LinE (PILE) mass flow sensor.

Research on the traffic flow measurement method of intelligent connected vehicles based on series and cascade technology

At present, the major cities in our country have entered the development of smart cities, the utilization rate and prevalence rate of intelligent connected cars in smart cities have reached a new peak,to manage the real-time traffic flow of intelligent connected vehicles effectively in smart cities. This paper proposes to calculate the flow of intelligent connected vehicles based on series cascade technology. Firstly, this paper collects multi-source heterogeneous data based on multiple collectors; Secondly, a generative adversarial network is employed for data fusion, synthetic data, closely mirroring the original, is generated for handling unknown variables during model training. Next, an autoencoder model generates input data for feature representation. Lastly, CatBoost, a regression model that leverages a cascaded machine learning algorithm, is utilized to forecast the traffic flow of intelligent connected vehicles in smart cities. The results show that the method based on CASCaded machine learning has remarkable effect in predicting smart city traffic flow and alleviating urban road congestion.

Flow prediction of irrigation well pump based on experimental and big data analytics

Abstract Currently, China’s agricultural irrigation consumes a huge amount of water, and traditional agricultural irrigation methods lead to low irrigation efficiency and serious water resource waste. Agricultural irrigation water management is also difficult to achieve refined management due to the lack of accurate monitoring of water use data and information in various irrigation areas. To find a more convenient flow measurement method, this paper proposes using data-driven monitoring of water pump characteristic parameters to predict flow rate. Three big data-based methods for predicting the flow rate of irrigation well pumps were compared, including RBF neural network, Support Vector Machine (SVM), and Extreme Learning Machine (ELM). The results indicate that the Extreme Learning Machine (ELM) model not only has short time consumption but also high prediction accuracy, laying the foundation for the application of big data technology in water management and high-quality irrigation water management that can save water.

Data-driven traffic sensor location and path flow estimation using Wasserstein metric

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.

Optimization of Space-Time image velocimetry based on deep residual learning

Space-Time Image Velocimetry (STIV) is considered to be a highly promising method for river flow measurement due to its safe and efficient features. However, the traditional STIV method is challenged to obtain stable and accurate results when disturbed by external environments such as obstacles, waves and noise. As a consequence, a residual network-based STIV method is constructed by combining STIV with deep learning. In the synthetic dataset validation, benefited from the unique residual module and the relation-aware global attention mechanism, the recognition accuracy of STI texture angle is as high as 99.9 %, which is much higher than that of the Efficient Net b3, ViT16 and VGG19 networks. In the real dataset, the proposed method shows stronger stability and lower error by compared with gradient tensor (GT) and fast Fourier transform (FFT) methods, whose overall recognition accuracy is above 90 % under all types of STI cases, angle error is within 2°, and flow velocity error is around 0.10 m/s. Furthermore, the model was validated by a one-year comparative measurement at the Qilijie hydrological station in 2022, and the results were compared with LSPIV, GT, and FFT methods. The R2 of the model to the measured values is 0.98, the RMSE is 0.17, and the MAE is 0.12 m/s, indicating that the method has sufficient accuracy and robustness for long-term stable and real-time monitoring of river flow.

A method of applying deep learning based optical flow algorithm to river flow discharge measurement

River flow discharge monitoring is one of the critical tasks performed at hydrological stations. The large-scale particle image velocimetry (LSPIV) method widely used in hydrological stations is often limited by a lack of floating objects and has a high computational complexity. The space-time image velocimetry method is susceptible to noise interference and requires high stability of the flow over time. This paper proposes a flow measurement method based on the recurrent all-pairs field transforms for optical flow (RAFT) algorithm. The 4D correlation volume pyramid in the RAFT network structure can effectively handle changing and complex flow conditions. The convolutional block attention module is introduced into the optical flow update module after the 4D correlation volume pyramid, enhancing the ability to capture complex flow surface information. Additionally, feature extraction adds deformable convolution to expand the receptive field of the flow image, which has better adaptability in non-rigid motion. To validate the effectiveness of the new method (RAFT-D-C), this paper conducts comparative experiments with both existing and new methods. The experimental results show that RAFT-D-C has relative errors of 2.13% and 4.41% for the average flow velocity of two rivers and relative errors of 2.19% and 3.05% for the total discharge, respectively. RAFT-D-C provides improved accuracy compared to other methods and requires less computational run time than the frequently used LSPIV method.

An indirect flow measurement method for bi-tandem axial piston pump based on leakage flow estimation

In pump-controlled hydraulic systems, configuring a flowmeter at a variable piston pump to monitor output flow causes additional losses. A method for indirect flow measurement of the bi-tandem axial piston pump based on flow leakage estimation is proposed to address this problem. The method first installs a displacement transducer at the actuator piston to collect data on the actuator piston position, outlet pressure, and shaft speed, then estimates leakage of key kinematic pairs, and finally considers flow ripple and fluid compressibility to form an indirect measurement for output flow of the pump. The indirect measurement accuracy under various typical operating conditions is analyzed. The results demonstrate that the mean absolute percentage error of the proposed indirect flow measurement method is less than 2.10%, and the method is effective with high measurement accuracy. The indirect measurement method avoids extra costs and energy consumption associated with configuring flowmeters in engineering hydraulic systems.

Comparative study of mobile acoustic uroflowmetry and conventional uroflowmetry: A systematic review and meta analysis.

Uroflowmetry is a noninvasive measurement of the volume of urine excreted over time. Conventional uroflowmetry has become the main modality of urine flow measurement within time. However, this method requires the patient to be present in the hospital or healthcare setting, thus sometimes making the patients feel uncomfortable to undergo the examination. This led to multiple measurements which are inconvenient for the patients. Mobile acoustic uroflowmetry (sono-uroflowmetry) has been proposed as an alternative method of urine flow measurement due to its portability. This study aimed to evaluate the accuracy and reliability of sono-uroflowmetry as compared to conventional uroflowmetry. Electronic databases searching were done using prespecified search strategy to retrieve articles related with uroflowmetry. In addition, hand-search strategy was used to identify additional articles. Studies with participants who had undergone sono-uroflowmetry were included. Voided volume, voiding duration, maximum flow rate, and average flow rate were identified and used to determine the outcomes of measurement. The quality of included articles was conducted using checklist for Diagnostic Test Accuracy Studies by JBI. Initial search yielded 335 articles with four additional papers identified through hand-searching process. Six papers were retrieved and further used in the narrative synthesis. Five studies enrolled male participants, while only one of the papers enrolled female participants as additional subgroup analysis. Therefore, the meta-analysis was performed by using only male participants. Based on the meta-analysis results, there were strong to very strong positive correlation in voided volume, voiding time, average flow, average flow rate, and maximum flow rate between sono and conventional uroflowmetry. Sonouroflowmetry showed significant positive correlations to conventional uroflowmetry, signifying its use as an alternative of conventional uroflowmetry.

Adaptive river flow measurement method based on spatiotemporal image velocimetry and optical flow.

This paper proposes an adaptive river discharge measurement method based on spatiotemporal image velocimetry (STIV) and optical flow to solve the problem of blurred texture features and limited measurement accuracy under complex natural environmental conditions. Optical flow tracking generates spatiotemporal images by following the flow mainstream direction of rivers with both regular and irregular natural banks. A texture similarity function filtering method effectively enhances spatiotemporal texture features. The proposed method is applied to a natural river, with measurement results from a propeller-type current meter used as truth values. It is evaluated and compared with three other methods regarding measurement accuracy, error, and other evaluation indices. The results demonstrate that the method significantly improves spatiotemporal image quality. Its estimation outcomes perform better across all evaluation metrics, enhancing the adaptability and accuracy of the flow measurement method.

Research on a New Flow Measurement for Intelligent Injection Tools

Intelligent separate injection technology can achieve continuous monitoring data of layered water Injection wells. So it can find out the inefficient water circulating formation timely. Then the water injection schemes can be adjusted in real-time to ensure the high water injection pass rate. It can also increase the working efficiency of the workers’ because of its automatic mode.But its prime cost is high, which limits its large-scale industrialization application in fine layered water injection wells. The key part is the intelligent water distributor in the intelligent separate injection technology. The cost of electromagnetic flow meter accounts for 38% in the intelligent water distributor. The paper intends to research on flow measurement without flow meter to reduce the high-cost. The pressure difference equation was established for flow rate and the opening ratio of the nozzle using the dimensionless analysis method. The empirical parameters of the equation were obtained through laboratory experiments. The pressure sensors are used to monitor the pressure of water injection wells while achieving the measurement of layered flow rate.The laboratory experiments results indicate that the calculated flow rate obtained by this method coincides well with the standard flow rate. The relative error is less than 12%. Then,it provides a new flow measurement method for intelligent injection tools. The most significant feature is that the flow meter is no longer mandatory for intelligent water distributors. Due to the cheaper cost that it provides a technical support for the large-scale industrialization application of intelligent separate injection technology in fine layered water injection wells.

MODELING OF STORM WATER DRAINAGE SYSTEM FOR OBUBRA LOCAL GOVERNMENT AREA, CROSS RIVER STATE OF NIGERIA

A model was developed for the Obubra catchment and subsequently applied in the study of the catchment basin. The model developed gave a multiple regression correlation of 0.994 at a significance level of 0.05. The model was calibrated using the first ten (10) set of values and a correction of 0.995 was obtained and was further verified using the second set of ten (10) values which gave a correlation of 0.995. the model was also validated using velocity- area method for water flow measurement having a relationship as Q= AV with a correlation of 1.0. Although, studies conducted in different areas have shown that a hundred percent (100%) success may not always be achieved in the urban environment yet, their damaging effects can be mitigated through management measures that can carefully designed by government or affected communities. Adequate constitution and routine maintenance of drainage channels be made, ensuring that the velocity satisfies the minimum requirement.

The Influence of a Manifold Structure on the Measurement Results of a PIV Flowmeter

The application of particle image velocimetry (PIV) technology for monitoring natural gas flow is a new method of flow measurement. Since the principle of this technology was proposed, there are still some potential issues. This article investigates the influence of a manifold structure on the measurement results of a PIV flowmeter. A comparison is performed between concentric and eccentric manifold structures, using a circular straight pipe as reference, in terms of the measurement error of the PIV flowmeter and the internal flow state of the natural gas. The results demonstrate that the manifold structure significantly affects the measurement reliability of the PIV flowmeter, especially the eccentric manifold structure. Under flow conditions ranging from 100 to 600 m3/h, the maximum measurement errors caused by the concentric and eccentric manifold structures are 2.49% and 3.05%, respectively, which show a noticeable increase compared to the maximum measurement error of 2.08% observed for the circular straight pipe. Additionally, the influence of the manifold structure on the downstream flow field is also evident, as the eccentric manifold structure increases the turbulence intensity of the downstream fluid by nearly twofold. The addition of a rectifier can effectively improve the flow state and enhance the measurement reliability of the PIV flowmeter. For the concentric manifold structure under the condition of a 600 m3/h flow rate, the inclusion of a rectifier produces highly accurate measurement results, similar to those obtained by an ultrasonic flowmeter, with an error value close to zero. This study provides technical support for further promoting the practical application of PIV flowmeters.

Measurement of high water-cut heavy oil flow based on differential pressure of swirling flow

Real-time measurement of heavy oil production is critical to ensure stable production. Due to the complex kinematic characteristics of heavy oil, existing methods cannot accurately measure its flow rate and water cut. In this paper, a novel method is proposed to measure the high water-cut heavy oil flow by using the differential pressure of the two-phase swirling flow in the pipe. For the swirling flow in the pipe, the radial differential pressure and the axial differential pressure exist simultaneously, which are very sensitive to the flow rate and water cut. The formation mechanism of the two kinds of differential pressure is analyzed theoretically, and their relationship with flow rate and water cut is studied by experiment and numerical simulation. The measurement model of heavy oil–water two-phase flow on the above relations is validated by field experiments. The radial differential pressure is only related to the two-phase flow rate, varying exponentially with the flow rate when the oil viscosity is greater than 10 000 mPa s. This characteristic is very useful for the heavy oil–water two-phase flow measurement. The axial differential pressure decreases with the increase in water cut in cases of water cut <85%, while it increases with the water cut in cases of water cut >85%. With the increase in water cut, the ratio of axial differential pressure to radial differential pressure first decreases and then increases. The relative errors of the established measurement model for flow rate and water cut are 0.19%–17.92% and 0.21%–15.5%, respectively, and more than 70% of the measurements with a relative error of less than 10%. The study of the heavy oil–water two-phase flow measurement method can optimize the measurement cost and accelerate the process of intelligent oilfield construction.

Different methods for hepatic flow measurements: a narrative review

Hepatic blood flow measurement constitutes an essential tool for successful hepatic surgery, especially in situations where graft patency needs to be controlled, such as for instance during and after liver transplantation. In addition, because of its complex intrinsic regulation, the hepatic circulation may be seriously affected in the perioperative setting not only because of systemic hemodynamic alterations but also secondary to the administration of various drugs including anesthetic agents. Unravelling and understanding such effects implies studies involving measurement of hepatic blood flow. Therefore, knowledge and understanding of various tools for estimation of hepatic blood flow is important for correct interpretation of findings from experimental and clinical studies before potential implementation in daily clinical practice. This review summarizes the different techniques, their strengths and limitations, and potential applications in clinical practice and research.

Wet gas flow metering by combining a vortex flowmeter with disturbance wave frequency

Based on a conductance-vortex meter dual-modality system, a novel wet gas flow measurement method is proposed, which combines a vortex meter with disturbance wave frequency. A negative relationship between the vortex signal quality and the disturbance wave frequency is found, on which basis a new segmented fast Fourier transform–continuous wavelet transform vortex frequency extraction method is proposed. Uniform exponential equations are derived for meter over-reading, taking into account the liquid-phase Reynolds and gas-phase Weber numbers, the disturbance wave Strouhal number, density ratio, and the liquid-phase Reynolds number. Finally, a Newton–Armijo-based wet gas metering model is developed by combining these two correlation equations. The percentage errors (PEs) of the gas flow in wet gas are within ±1.0% error bands with uncertainty of 0.56%. The full-scale PEs of liquid flow are within ±10% error bands with uncertainty of 4.71%. The disturbance wave frequency is used to correct the meter over-reading. As no liquid film thickness calibration is needed, the proposed method has low requirements on the conductivity of the medium and hence the application scope of media could be expanded.

Flow Measurement Methods in Small Tributaries of the Teles Pires River, Southern of the Amazon Hydrological Region

The intensification of conflicts associated with the use of water in the transition region of the Cerrado and Amazon biomes caused by population and economic growth, combined with the interest in generating energy from hydroelectric plants, raise the need to quantify the surface water availability of rivers contributing with different drainage areas. The present study estimated and compared in loco measurements of liquid flow (QL) and the depth of rivers in the Teles Pires river basin by reference methods (MLN-7 hydrometric windlass and metal rod/winch) and by Acoustic Current Profiler by Doppler Effect (ADCP RiverRay), in this last method the uncertainty estimate of the total measurement time by ADCP was evaluated. Field measurements were carried out at monthly intervals between March 2020 and October 2021, seeking to represent the water seasonality and depth and QL variations in the cross-sections of the Caiabi 1 and 2, Celeste, Preto and Renato rivers. The evaluated rivers had a net flow between 3.48 and 60.78 m3 s−1 by the windlass and between 2.66 and 54.30 m3 s−1 by the ADCP, while the depths obtained were from 0.17 to 6.34 m by the rod/winch and from 0.65 to 6.20 m by the ADCP. The methods resulted in similar measurements of net flow and depth in each of the cross-sections, and the statistical performance of the linear regression model was satisfactory with a Willmott concordance index of 0.9977 and 0.9819 for estimates of QL and of the depth of the cross-sections, respectively. The ADCP accurately measured the net discharge and depth in shallow (up to 6.5 m) cross-sections of the Teles Pires River relative to the reference method. Determining the total measurement time and pairs of transects to obtain accurate QL by ADCP depends on the hydraulic characteristics of the watercourses.

P045 Noxturnal cRIP: A Comparative Analysis of Sensors for the Identification of Respiratory Events in Polysomnography

Abstract Introduction AASM recommends the use of nasal pressure (NP), oronasal thermal flow (TF), and respiratory inductance plethysmography (RIP) for detecting and characterising respiratory events in polysomnography. The use of both NP and TF sensors is reported to be more accurate in respiratory event identification than either alone. However, these sensors can be unreliable if dislodged and cause discomfort. Noxturnal calibrated RIP flow (cRIPflow), derived from RIP, may provide a non-invasive alternative method for flow measurement in identification of respiratory events. Method Respiratory scoring was performed manually by a single experienced scorer on 10 diagnostic sleep studies under AASM standards. Scoring was repeated using three different measurements for each study: cRIPflow only, Thermistor (Th) only and both Th+NP (AASM recommendation). Apnoea hypopnoea index (AHI), central apnoea index (CAI), obstructive apnoea index (OAI), mixed apnoea index (MAI) and hypopnoea index (HI) were calculated and paired t-test analysis utilised for comparison between measurements. Results No statistical differences were identified in comparison of cRIPflow with Th or Th+NP in respiratory event identification: CAI (cRIPflow 3.2/hr±7.1, Th 3.9/hr±8.9, Th+NP 3.1/hr±7.5), OAI (cRIPflow 6.1/hr±6.8, Th 5.3/hr±8.2, Th+NP 6.7/hr±8.9), MAI (cRIPflow 5.2/hr±9.7, Th 4.3/hr±8.7, Th+NP 4.7/hr±9.2), or HI (cRIPflow 12.5/hr±13.9, Th 11.1/hr±10.5, Th+NP 10.4/hr±10.7). There was a statistical difference for AHI (cRIPflow 26.9/hr±26.6, Th 24.5/hr±26.4, Th+NP 25.0/hr±26.5). Discussion This study suggests cRIPflow may provide an alternative measurement in the detection and characterisation of respiratory events, however further analysis with larger sample size would provide more insight into sensitivity and specificity of this method.

Real Time Measurement of Multiphase Flow Velocity using Electrical Capacitance Tomography

Accurate and real-time measurement of fluid flow velocity is crucial in various industrial processes, especially when dealing with multiple phase fluids. Traditional flow measurement methods often struggle to accurately quantify the velocity of complex multiphase flows within pipes. This challenge necessitates the exploration of innovative techniques capable of providing reliable measurements. This paper proposes the utilization of Electrical Capacitance Tomography (ECT) as a promising approach for measuring the velocity of multiple phase fluids in pipes. The ECT technique involves the non-intrusive imaging of the electrical capacitance distribution within the pipe. By utilizing an array of electrodes placed around the pipe circumference, the capacitance distribution can be reconstructed, offering insight into the fluid flow patterns. By analyzing the temporal changes in the capacitance distribution, the velocity of different phases within the pipe can be estimated. To achieve accurate velocity measurements, an ECT system needs to account for the complexities introduced by multiphase flows. Various image reconstruction algorithms, such as linear back-projection and iterative algorithms like Gauss-Newton and Levenberg-Marquardt, are employed to reconstruct the capacitance distribution. Additionally, advanced signal processing techniques, such as cross-correlation analysis and time-difference methods, are used to extract velocity information from the reconstructed images. This paper presents an experimental investigation of measuring the velocity of multiple-phase fluids in pipes using the ECT technique. The study aims to address the challenges associated with different flow regimes, fluid properties, and pipe geometries by exploring advancements in electrode design, system calibration, and data processing techniques to enhance the accuracy and robustness of ECT-based velocity measurements.