Flow Rate Estimation Research Articles

Flow characteristics of gas and liquid in pipeline revealed by machine learning on distributed acoustic sensing data

The two-phase flow rates of gas and liquid in a pipeline are crucial parameters for optimizing gas-oil production strategies and ensuring the reliability of gas-oil transportation systems. Although available measurement techniques, such as various flow meters, offer accurate flow rate data, they might face limitations in providing distributed and real-time information at multiple points. Distributed Acoustic Sensing (DAS) offers a viable alternative for long-term, multipoint dynamic monitoring of the flow. However, the data acquired through fiber optic monitoring techniques are often difficult to analyze and process in real-time, while machine learning offers automatic identification of complex patterns and relationships within the data, enabling more precise predictions and classifications. To evaluate the feasibility of DAS technology combined with machine learning methods to estimate the gas-liquid flow rate in pipelines, an experimental loop that utilized DAS was developed to measure gas-liquid two-phase flow signals in pipelines. The machine learning method was then applied to analyze the DAS signals, based on which models were established to predict flow rates and regimes. Furthermore, validation experiments were conducted to assess the predictive performance of these models. Compared to the actual flow rates measured by electronic flowmeters, the results by integration of DAS and machine learning show the predictive accuracy of two models reach 97%. In the subsequent validation experiments, both the goodness of fit for the flow rate prediction model and accuracy for the flow regime prediction model exceeded 85%. Thus, compared to current flow measurement methods, the integration of DAS and machine learning not only provides accurate flow rate estimations but also offers available prediction of flow regimes, enhancing the measurement capabilities and technology insights.

A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network

Accurate estimation of flow rate in gas–liquid two-phase flow is crucial for various industrial processes. How to accurately estimate flow rate remains a challenging problem. Previously, deep learning-based methods focused on a few human-set points with single task learning. In addition, the data were not denoised. In this study, a flow rate estimation method based on a filter-enhanced convolutional neural network (FECNN) is proposed for gas–liquid two-phase flow. The method leverages multimodal data from a Venturi tube and an electrical capacitance tomography (ECT) sensor as input, utilizing multilayer perceptron (MLP) to fuse data. Subsequently, a learnable filter module is employed to attenuate noise adaptively, followed by multiscale convolutional neural network (MSCNN) extraction of flow rate features at different scales. Finally, the method enables estimate each single-phase flow rate simultaneously through multi-task learning (MTL). The adaptive noise attenuation capabilities of the learnable filter module are demonstrated, and the ability of the proposed MSCNN to capture multiscale flow rate features through multiple comparative experiments is shown. Additionally, a qualitative comparison with recent flow rate estimation methods is provided. Overall, this study demonstrates the effectiveness and superiority of the proposed FECNN in flow rate estimation.

Dynamical accretion flows

Context. Investigating the flow of material along filamentary structures towards the central core can help provide insights into high-mass star formation and evolution. Aims. Our main motivation is to answer the question of what the properties of accretion flows are in star-forming clusters. We used data from the ALMA Evolutionary Study of High Mass Protocluster Formation in the Galaxy (ALMAGAL) survey to study 100 ALMAGAL regions at a ∼1″ resolution, located between ∼2 and 6 kpc. Methods. Making use of the ALMAGAL ∼1.3 mm line and continuum data, we estimated flow rates onto individual cores. We focus specifically on flow rates along filamentary structures associated with these cores. Our primary analysis is centered around position velocity cuts in H2CO (30, 3–20, 2), which allow us to measure the velocity fields surrounding these cores. Combining this work with column density estimates, we were able to derive the flow rates along the extended filamentary structures associated with cores in these regions. Results. We selected a sample of 100 ALMAGAL regions, covering four evolutionary stages from quiescent to protostellar, young stellar objects (YSOs), and HII regions (25 each). Using a dendrogram and line analysis, we identify a final sample of 182 cores in 87 regions. In this paper, we present 728 flow rates for our sample (4 per core), analysed in the context of evolutionary stage, distance from the core, and core mass. On average, for the whole sample, we derived flow rates on the order of ∼10−4 M⊙ yr−1 with estimated uncertainties of ±50%. We see increasing differences in the values among evolutionary stages, most notably between the less evolved (quiescent and protostellar) and more evolved (YSO and HII region) sources and we also see an increasing trend as we move further away from the centre of these cores. We also find a clear relationship between the calculated flow rates and core masses ∼M2/3, which is in line with the result expected from the tidal-lobe accretion mechanism. The significance of these relationships is tested with Kolmogorov–Smirnov and Mann-Whitney U tests. Conclusions. Overall, we see an increasing trend in the relationships between the flow rate and the three investigated parameters, namely: evolutionary stage, distance from the core, and core mass.

Experimental study using phantom models of cerebral aneurysms and 4D-DSA to measure blood flow on 3D-color-coded images.

The current 3D-iFlow application can only measure the arrival time of contrast media through intensity values. If the flow rate could be estimated by 3D-iFlow, patient-specific hemodynamics could be determined within the scope of normal diagnostic management, eliminating the need for additional resources for blood flow rate estimation. The aim of this study is to develop and validate a method for measuring the flow rate by data obtained from 3D-iFlow images - a prototype application in Four-dimensional digital subtraction angiography (4D-DSA). Using phantom model and experimental circuit with circulating glycerin solution, an equation for the relationship between contrast media intensity and flow rate was developed. Applying the equation to the aneurysm phantom models, the derived flow rate was evaluated. The average errors between the derived flow rate and setting flow rate became larger when the glycerin flow and the X-rays from the X-ray tube of the angiography system were parallel to each other or when the measurement point included overlaps with other contrast enhanced areas. Although the error increases dependent on the imaging direction and overlap of contrast enhanced area, the developed equation can estimate the flow rate using the image intensity value measured on 3D-iFlow based on 4D-DSA.

A Flow Rate Estimation Method for Gas–Liquid Two-Phase Flow Based on Transformer Neural Network

A Flow Rate Estimation Method for Gas–Liquid Two-Phase Flow Based on Transformer Neural Network

Performance evaluation of a nonstandard Parshall flume and comparison of experimental techniques for its verification in a drinking water treatment plant

ABSTRACT This article investigates a nonstandard Parshall flume (Pf) to assess the impact of engineering deficiencies on flow rate (Q) estimation and compare experimental techniques for evaluating its performance in Q quantification and velocity gradient (G) appraisal during rapid mixing. Considering construction and instrumentation issues in the studied Pf, Q estimates were 10% lower than those derived from the standard rating curve (SRC), with 9% of this discrepancy attributable to the high placement of the stilling well scale and 1% to the position of the connecting pipe to the gauging section. When contrasting Q estimates from the SRC to the current rating curve in use and a set of rating curves fitted using salt dilution gauging (SDG), acoustic Doppler velocimetry (ADV), and numerical modeling (CFD), the average differences were −10.2, −8.3, −2.2, and 0.2%, respectively, for the current rating curve in use and those fitted to ADV, SDG, and CFD data. Considering only the instrumental measuring techniques, the SDG exhibited superior fittings (R2 = 0.99), followed by ADV (R2 = 0.95). Calculations of G aligned (9.3%) with CFD estimates support these methods as reliable tools for evaluating and verifying nonstandard Pf performance.

Improvement of field falling-head test and determination of hydraulic conductivity using Darcy’s equation

This study presents a novel permeameter design for field falling-head tests to determine vertical hydraulic conductivity (K) using Darcy's equation. The design features an open-ended standpipe with two ports for simultaneous measurement of hydraulic heads at both ends of the sediment column, enabling direct estimation of flow rate (q) and hydraulic gradient (i). Flow rate is calculated by differentiating the best-fit curve for water level change above the sediment, and the hydraulic gradient is derived from the head difference between the ports. Laboratory and field tests consistently demonstrated a strong linear relationship between q and i (R2 > 0.999), validating the applicability of Darcy's equation for this new permeameter design. The K values obtained using the proposed method matched those obtained using the Hvorslev equation method. Furthermore, the design allows for continuous measurement of heads after the falling-head permeameter test, facilitating the collection of time series data for the hydraulic gradient. When combined with a pre-determined K value, this enables calculation of time series for the seepage rate across the surface water/sediment interface. To demonstrate this capability, preliminary tests were conducted using commercially-available pressure transducers to monitor heads and obtain seepage time series. The results of these tests are also presented.

End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams

End-to-End Traffic Flow Rate Estimation From MPEG4 part-2 Compressed Video Streams

Back to Bernoulli: a simple formula for trans-stenotic pressure gradients and retrospective estimation of flow rates in cerebral venous disease

BackgroundVenous sinus stenosis can be associated with cerebrovascular disorders. Understanding the role of blood flow disturbances in these disorders is often hampered by the lack of patient-specific flow rates. Our...

Assessment of Municipal Solid Waste Management Scenarios in Metro Manila Using the Long-Range Energy Alternatives Planning-Integrated Benefit Calculator (LEAP-IBC) System

Short-lived climate pollutants (SLCPs) and municipal solid wastes (MSWs) have been found to be viable sources of clean energy. This study integrates the Intergovernmental Panel on Climate Change (IPCC) guidelines for methane flow rate estimation in the software Long-Range Energy Alternatives Planning-Integrated Benefit Calculator (LEAP-IBC) system to estimate and project the methane emissions coming from the waste generated by Metro Manila, disposed in sanitary landfills. It aims to analyze the environmental impacts of the emissions coming from the non-energy sector using the IBC feature of LEAP and by developing two scenarios with 2010 and 2050 as the base and end years: the baseline and methane recovery scenario, where the latter represents the solid waste management undertaken to counter the emissions. Under the baseline, 97.30 million metric tonnes of methane emissions are expected to be produced and are predicted to continuously increase. In the same scenario, the cities of Quezon, Manila, and Caloocan account for the biggest methane emissions. On the other hand, in the methane recovery scenario, the methane emissions are expected to have a decline of 36% from 127.036 to 81.303 million metric tonnes by 2025, 52% from 135.358 to 64.972 million metric tonnes by 2030, and 54% from 150.554 to 69.254 million metric tonnes by 2040. For the 40-year projection of the study under the 100-year global warming potential analysis, a total of 10,249 million metric tonnes of CO2 equivalent is avoided in the methane recovery compared to the BAU, and a maximum of 0.019 °C temperature increase can also be avoided. Moreover, electricity costs without LFG technology increase from 2.21 trillion to 8.75 trillion, while costs with LFG technology also rise but remain consistently lower, ranging from 2.20 trillion to 8.74 trillion. This consistent reduction in electricity costs underscores the long-term value and importance of adopting LFG technology, even as its relative savings impact diminishes over time. Finally, the fixed effects and random effects panel data regression analysis reinforces and asserts that the solid waste management is really improved by means of the methane recovery technology, leading the methane emissions to decrease.

Cerebral Oxygen Saturation Associates with Changes in Oxygen Transport Parameters during Cardiopulmonary Bypass.

(1) Background: Adequate organ perfusion during cardiopulmonary bypass (CPB) requires accurate estimation and adjustment of flow rates which conventional methods may not always achieve. Perioperative monitoring of cerebral oxygen saturation (ScO2) may detect changes in oxygen transport. This study aims to compare estimated and measured perfusion flow rates and assess the capacity of ScO2 to detect subtle changes in oxygen transport during CPB. (2) Methods: This observational study included 50 patients scheduled for elective coronary artery bypass grafting (CABG) surgery, all of whom provided written informed consent. Perfusion flow rates were estimated using the DuBois formula and measured using echocardiography and a flow probe in the arterial line of the CPB system. ScO2 was continuously monitored, alongside intermittent measurements of oxygen delivery and extraction ratios. (3) Results: Significant discrepancies were found between estimated flow rates (5.2 [4.8-5.5] L/min) and those measured at the start of the surgery (4.6 [4.0-5.0] L/min). These discrepancies were flow rate-dependent, being more pronounced at lower perfusion rates and diminishing as rates increased. Furthermore, ScO2 showed a consistent correlation with both oxygen delivery (r = 0.48) and oxygen extraction ratio (r = 0.45). (4) Conclusions: This study highlights discrepancies between estimated and actual perfusion flow rates during CPB and underscores the value of ScO2 monitoring as a continuous, noninvasive tool for maintaining adequate organ perfusion, suggesting a need for improved, patient-tailored perfusion strategies.

Hydroacoustic Monitoring of Mayotte Submarine Volcano during Its Eruptive Phase

Submarine volcanoes are more challenging to monitor than subaerial volcanoes. Yet, the large eruption of the Hunga Tonga-Hunga Ha’apai volcano in the Tonga archipelago in 2022 was a reminder of their hazardous nature and hence demonstrated the need to study them. In October 2020, four autonomous hydrophones were moored in the sound fixing and ranging channel 50 km offshore Mayotte Island, in the North Mozambique Channel, to monitor the Fani Maoré 2018–2020 submarine eruption. Between their deployment and July 2022, this network of hydrophones, named MAHY, recorded sounds generated by the recent volcanic activity, along with earthquakes, submarine landslides, marine mammals calls, and marine traffic. Among the sounds generated by the volcanic activity, impulsive signals have been evidenced and interpreted as proxy for lava flow emplacements. The characteristics and the spatio-temporal evolution of these hydroacoustic signals allowed the estimation of effusion and flow rates, key parameters for volcano monitoring. These sounds are related to the non-explosive quenching of pillow lavas due to the rapid heat transfer between hot lava and cold seawater, with this process releasing an energy equivalent to an airgun source as used for active seismic exploration. Volcano observatories could hence use autonomous hydrophones in the water column to detect and monitor active submarine eruptions in the absence of regular on-site seafloor survey.

A Modified Manning’s Equation for Estimating Flow Rate in Grass Swales under Low Inflow Rate Conditions

As green infrastructure has evolved, grass swales have become integral components of stormwater management. Manning’s equation is commonly used to describe the hydraulic characteristics of grass swales. However, due to flow loss from infiltration, grass swales often deviate from the assumptions of Manning’s equation, potentially leading to significant errors in grass swale flow rate calculations. In this study, we systematically investigated changes in flow rates in grass swales under various constant inflow rate conditions. The results indicated that the suitability of using Manning’s equation to estimate flow rate in grass swales varies with inflow rate. At an inflow rate of 3.00 m3/h, the discrepancy between the measured and the estimated flow rates by Manning’s equation was the smallest, ranging from −0.24 to 0.19 m3/h. At lower inflow rates (1.00 to 2.00 m3/h), Manning’s equation underestimated the flow rates by 0.16 to 0.47 m3/h; at higher rates (4.00 m3/h), it overestimated the flow rates by 0.01 to 0.61 m3/h. Considering infiltration losses as the primary cause of these errors, we proposed an improved Darcy’s formula for estimating the infiltration rates in grass swales, along with a modified Manning’s equation for more accurate flow rate calculations. The modified Manning’s equation provides enhanced accuracy in calculating flow rates in grass swales compared to the traditional version.

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Salinity and flow pattern independent flow rate measurement in a gas-liquid flow with optimum feature selection and novel detection geometry using ANNs

This paper presents a study on the salinity determination and flow rate estimation in a two-phase air-water system. For this purpose, an automated air-water test loop capable of generating different flow patterns in a horizontal pathway was utilized to do numerous experiments at varying flow rates. A nuclear measuring setup, comprising Cs-137 and Am-241 as radiation sources, one NaI (Tl) scintillation detector to register transmission counts of Cs and three scintillator detectors for registering transmission and scattering counts from Am, was prepared. A pressure drop pipe was also employed for flow rate measurement, with MLPs were selected as the processing element. Notable innovations of this paper can be considered from two perspectives. Firstly, a novel paradigm in nuclear measurement geometry. This novelty uses the benefits of three scintillator detectors to extract features with the maximum potential for classifying and determining salinity. Secondly, there is a new method in data processing and utilizing optimum features to achieve the best performance in predicting flow rates. Results in flow rate prediction independent of salinity and flow regime indicate that the proposed paradigm and method are reliable for using in industrial fields related to multi-phase metering.

Deep learning-assisted dual-modal tomography for phase flow rate estimation in two-phase oil-water flow systems

Accurately estimating phase flow rates in multiphase systems is crucial for many industries, where precise measurements are essential for operational efficiency and safety. Addressing this issue, this paper introduces an approach that employs deep learning-assisted dual-modal electromagnetic flow tomography (EMFT) and electrical tomography (ET) to predict both oil and water flow rates in two-phase oil-water flows. To facilitate the generation of the data, we first simulate diverse flow conditions using COMSOL Multiphysics software and the convection–diffusion equation, aiming to create a realistic representation of two-phase oil-water flows. The dual-modal system measurement data, generated from these simulations and simulated by using a dense finite element mesh, provide reliable inputs for the deep learning model. Moreover, this study also integrates experimental data into both the training and testing phases, improving the ability of the proposed approach to estimate flow rates accurately in practical investigations. The results from laboratory experiments demonstrate the potential of the deep learning-assisted dual-modal ET and EMFT approach in effectively resolving the challenges of estimating flow rates in two-phase oil-water flow systems. By combining the deep learning capabilities with dual-modal tomography, this study offers valuable insights for future applications and represents a significant step forward in the field of multiphase flow rate estimation.

Free Overfall for Discharge Measurement in Channels of Different Shapes: A Review

The end drop in open channels can be considered as a simple and useful tool for the measurement of flow rates flowing in open channels of different shapes. The depth at channel end can be related to the critical depth in the approaching channel and denoted as (EDR). The (EDR) is an essential parameter for measuring end drop discharge (EDD) in open channels. The main target of this paper is to review the previous works done on the end drop in different shapes of channels using the properties of the free end drop flow as basic criteria for the estimation of (EDR) and (EDD). Useful relationships between (EDR) and channel slope are presented which are the outcome results of previous researches done on smooth and rough open channels of different shapes (rectangular, trapezoidal, circular and triangular). Moreover, the correlations of (EDD) depending on end drop depth and characteristics of the upstream approaching channel are presented for channels of different shapes for subcritical and supercritical states of flow. Still there is no unique theoretical solution for the problem of using end depth of free drop as a key for the estimation of flow rates in open channels which needs more theoretical and experimental studies and works on this important field of research.

Flow Rates of Resting whole Saliva of Diabetic Patients in Relation to Age and Gender

Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia, present with symptoms such as thirst, polyuria, and weight loss. The oral complications associated with this disease include dry mouth due to decrease in salivary flow and enlargement of the salivary glands.Cross sectional study conducted to estimate flow rates of resting whole saliva in 150 subjects (100 diabetic patients of both types I & II as experimental group, and 50 subjects as control group) which correlated with age and gender.The subjects were divided into three main groups: control group and two diabetic groups according to the types of diabetes mellitus( I& II).Unstimulated saliva were collected, and salivary flow rate was measured by establishing the time factor (5 minutes), after estimating the volume of collected saliva the salivary flow rate was calculate as ml/min.Results indicated that poorly controlled diabetic patients had more diminished salivary flow rate when compared with good controlled diabetic and non-diabetic subjects. Female diabetic patients older than 45 years, revealed a lowered salivary flow rate when compared with male diabetic patients younger than 45 years.

Flow Rate and Volume Estimates from Variable Frequency Drive Operated Drainage Sump Pumps

Highlights This article provides an overview of operations and benefits of using a variable frequency drive to operate agricultural subsurface drainage system sump pumps. A methodology is proposed to monitor flow rates leaving agricultural subsurface drainage pump stations via a MATLAB algorithm. The results of this research were mixed because the algorithm estimates did not always match with the corresponding flowmeter measurements. Abstract. Subsurface drainage plays a crucial role in excess water removal through perforated pipes buried in the soil. The Red River Valley of eastern North Dakota and west central Minnesota often requires pumped drainage outlets due to its flat topography, with many modern systems incorporating variable frequency drives (VFD) for improved efficiency. VFDs adjust pump speeds to match drainage needs, but this dynamic operation complicates accurate flow rate estimation for edge-of-field monitoring. This project aimed to devise an automated method for calculating flow rate estimates from VFD pumping systems involving tank geometry, operational water levels, and pump duty cycling. Linear regressions with calibration data from a transit-time ultrasonic flowmeter produced mixed outcomes. Some linear regressions revealed errors in calculated versus measured flow rate of =14%, while other regressions showed errors nearing 40%. To address this, future efforts should explore methods incorporating the pump’s electrical characteristics. This approach would provide a more accurate representation of the unstable and intricate pumping operations during transient conditions. Keywords: Edge-of-field monitoring, Flow rate, Pump, Red River Basin, Subsurface drainage, Variable frequency drive