Pub Date : 2026-01-10DOI: 10.1016/j.flowmeasinst.2026.103199
Jiangbo Qian , Shuo Wang , Ao Liu , Zhijian Zhang , Guanghai Lu
Real-time, accurate, and non-contact measurement of pulverized coal concentration (PCC) in primary air ducts is critical for the safe and efficient operation of coal-fired boilers. Existing microwave attenuation-based methods suffer from narrow bandwidth, cumbersome installation, or lack of dynamic experimental validation. To address these limitations, this study proposes a PCC measurement system based on microwave attenuation theory. First, a mathematical model for the equivalent complex permittivity of the air-pulverized coal two-phase mixture was established by applying integral modification to the Maxwell-Garnett heterogeneous dielectric equation. After incorporating the dielectric parameters of lignite into the model, the dependence of the complex permittivity of the air-lignite mixture on electric field frequency and coal concentration was determined. Based on microwave attenuation theory, a relationship between microwave attenuation and dielectric permittivity was derived, thus establishing the theoretical foundation for concentration measurement. Second, a dual-ridged horn antenna operating in the 2–10 GHz range was designed and optimized, featuring flexible installation and stable radiation performance. Finally, dynamic experiments were conducted under reflective and transmissive modes, simulating actual power plant scenarios with six PCC levels (0–1.0 kg/kg). The maximum relative deviations between the measured values and the true concentrations obtained by the reflection and transmission arrangements were 8.59 % and 7.31 %, respectively. Results confirm that the proposed system achieves wide bandwidth, flexible deployment, and reliable accuracy, verifying its feasibility for gas-solid two-phase flow concentration measurement.
{"title":"Research on the measurement method of pulverized coal concentration based on microwave loss","authors":"Jiangbo Qian , Shuo Wang , Ao Liu , Zhijian Zhang , Guanghai Lu","doi":"10.1016/j.flowmeasinst.2026.103199","DOIUrl":"10.1016/j.flowmeasinst.2026.103199","url":null,"abstract":"<div><div>Real-time, accurate, and non-contact measurement of pulverized coal concentration (PCC) in primary air ducts is critical for the safe and efficient operation of coal-fired boilers. Existing microwave attenuation-based methods suffer from narrow bandwidth, cumbersome installation, or lack of dynamic experimental validation. To address these limitations, this study proposes a PCC measurement system based on microwave attenuation theory. First, a mathematical model for the equivalent complex permittivity of the air-pulverized coal two-phase mixture was established by applying integral modification to the Maxwell-Garnett heterogeneous dielectric equation. After incorporating the dielectric parameters of lignite into the model, the dependence of the complex permittivity of the air-lignite mixture on electric field frequency and coal concentration was determined. Based on microwave attenuation theory, a relationship between microwave attenuation and dielectric permittivity was derived, thus establishing the theoretical foundation for concentration measurement. Second, a dual-ridged horn antenna operating in the 2–10 GHz range was designed and optimized, featuring flexible installation and stable radiation performance. Finally, dynamic experiments were conducted under reflective and transmissive modes, simulating actual power plant scenarios with six PCC levels (0–1.0 kg/kg). The maximum relative deviations between the measured values and the true concentrations obtained by the reflection and transmission arrangements were 8.59 % and 7.31 %, respectively. Results confirm that the proposed system achieves wide bandwidth, flexible deployment, and reliable accuracy, verifying its feasibility for gas-solid two-phase flow concentration measurement.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103199"},"PeriodicalIF":2.7,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-10DOI: 10.1016/j.flowmeasinst.2026.103194
Benjamin Peric , Marc Schuler , Michael Engler , Katja Gutsche , Peter Woias
This study introduces a data-driven model that enables the direct replacement of volume flow sensors in external gear pump applications. As this study aims to meet the requirements of real-world industrial scenarios, it is necessary to utilize a small data approach to demonstrate an applicable and scalable solution. The method improves through a data augmentation process based on fundamental physical laws, reducing the need for an extensive data set. A neural network predicts the volume flow within the pump's operating points over its entire operating range. The control architecture, including failure mechanisms, is presented, and the execution time is validated under real conditions on the microcontrollers. Two different fluid systems are investigated with three different types of external gear pumps and validated over the entire operating range of the fluid machinery. The methodology achieves a mean absolute percentage error of 1.53 % considering the output volume flow of the pump systems.
{"title":"Flow sensors substitution using neural networks with small data for external gear pumps","authors":"Benjamin Peric , Marc Schuler , Michael Engler , Katja Gutsche , Peter Woias","doi":"10.1016/j.flowmeasinst.2026.103194","DOIUrl":"10.1016/j.flowmeasinst.2026.103194","url":null,"abstract":"<div><div>This study introduces a data-driven model that enables the direct replacement of volume flow sensors in external gear pump applications. As this study aims to meet the requirements of real-world industrial scenarios, it is necessary to utilize a small data approach to demonstrate an applicable and scalable solution. The method improves through a data augmentation process based on fundamental physical laws, reducing the need for an extensive data set. A neural network predicts the volume flow within the pump's operating points over its entire operating range. The control architecture, including failure mechanisms, is presented, and the execution time is validated under real conditions on the microcontrollers. Two different fluid systems are investigated with three different types of external gear pumps and validated over the entire operating range of the fluid machinery. The methodology achieves a mean absolute percentage error of 1.53 % considering the output volume flow of the pump systems.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103194"},"PeriodicalIF":2.7,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.flowmeasinst.2026.103198
Qi Liu , Kaiwen Deng , Yongcao Gao , Yuzhen Jin
The cavitation phenomenon is extremely concerned in an aviation fuel gear pump, and its induced cavitation erosion can dominate the reliability of an aviation fuel gear pump and even the safety of the aviation fuel transportation system. In this study, the cavitation and cavitation erosion characteristics were studied in an aviation gear pump by considering the influence of rotational speeds (from 2800 r/min to 11500 r/min) and the fuel temperatures (from −20 °C to 110 °C). The experiment for testing output performance of the gear pump was built and the numerical method employing full cavitation model was verified to reveal the cavitation evolution and cavitation erosion. Cross sectional velocity and pressure in the gear pump and their amplitudes in different areas of the engagement region were comparatively analyzed for different operation conditions. Great pressure variation was produced from the fuel trapped area to the depressurization area, and it was more sensitive to the rotational speed than the fuel temperature. Dynamic evolution of cavitation in the gear rotation process was revealed. Cavitation occurrence was revealed near the suction surface of a gear tooth in the depressurization area where great pressure releasing. Dimensionless cavitation volume was defined to present the cavitation degree, whose average value varying with the rotational speed and the fuel temperature were determined by linear fitting. Indicated by the damage energy, the cavitation erosion caused by the cavitation bubble evolution was analyzed by considering the influence of local flow velocity. It was found that the distribution of cavitation damage power on a tooth surface was greatly influenced by the shear stress. Great shear stress overlapping with concentrated bubble collapsing energy was revealed as the mechanism of accelerating cavitation erosion on a tooth surface. Intense cavitation erosion could be easily triggered under a high rotational speed of 11500 r/min and even with a low fuel temperature of −20 °C. This study should be helpful for understanding cavitation characteristics and providing the insight to prevent cavitation erosion in the aviation industry.
{"title":"Study on the characteristics of cavitation and cavitation erosion in an aviation fuel gear pump","authors":"Qi Liu , Kaiwen Deng , Yongcao Gao , Yuzhen Jin","doi":"10.1016/j.flowmeasinst.2026.103198","DOIUrl":"10.1016/j.flowmeasinst.2026.103198","url":null,"abstract":"<div><div>The cavitation phenomenon is extremely concerned in an aviation fuel gear pump, and its induced cavitation erosion can dominate the reliability of an aviation fuel gear pump and even the safety of the aviation fuel transportation system. In this study, the cavitation and cavitation erosion characteristics were studied in an aviation gear pump by considering the influence of rotational speeds (from 2800 r/min to 11500 r/min) and the fuel temperatures (from −20 °C to 110 °C). The experiment for testing output performance of the gear pump was built and the numerical method employing full cavitation model was verified to reveal the cavitation evolution and cavitation erosion. Cross sectional velocity and pressure in the gear pump and their amplitudes in different areas of the engagement region were comparatively analyzed for different operation conditions. Great pressure variation was produced from the fuel trapped area to the depressurization area, and it was more sensitive to the rotational speed than the fuel temperature. Dynamic evolution of cavitation in the gear rotation process was revealed. Cavitation occurrence was revealed near the suction surface of a gear tooth in the depressurization area where great pressure releasing. Dimensionless cavitation volume was defined to present the cavitation degree, whose average value varying with the rotational speed and the fuel temperature were determined by linear fitting. Indicated by the damage energy, the cavitation erosion caused by the cavitation bubble evolution was analyzed by considering the influence of local flow velocity. It was found that the distribution of cavitation damage power on a tooth surface was greatly influenced by the shear stress. Great shear stress overlapping with concentrated bubble collapsing energy was revealed as the mechanism of accelerating cavitation erosion on a tooth surface. Intense cavitation erosion could be easily triggered under a high rotational speed of 11500 r/min and even with a low fuel temperature of −20 °C. This study should be helpful for understanding cavitation characteristics and providing the insight to prevent cavitation erosion in the aviation industry.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103198"},"PeriodicalIF":2.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.flowmeasinst.2026.103192
Thaer Hashem , Jaafar S. Maatooq , Ahmed Y. Mohammed
This study aims to provide an innovative development the traditional steps by creating holes of dimensions, numbers and geometric arrangement to enable the flow to penetrate through and overlaps with the main flow passing over the crest, leading to an increase in turbulence areas responsible for increasing the chance of air interanment, which is meaning more increasing in the dissipation of kinetic energy. This new approach has been tested numerically using CFD and verified experimentally. Different scenarios of upstream flow conditions, and geometric properties of holes, perforation ratio, number of rows, spacing in between, and alignment have been adopted in the test. The results show that a lower perforation ratio, a single straight row, and a single step distance yield the best performance for the aim among all adopted scenarios. Compared to traditional design, the result indicated that the energy dissipation increased to approximately 130 % and the gained evacuated discharge up to 150 % for identical upstream flow conditions. Furthermore, Local pressure distribution reduction up to 85 %. Turbulent intensity, turbulent dissipation, flow field pressure fluctuation, and volume of fluid fraction perform well with decreasing perforation ratio, number of rows, and step distance, with straight alignment. This study will be beneficial for safely removing high excess flow while maintaining a safe downstream environment.
{"title":"Novel kinetic energy dissipation CFD-based simulation over holed-steps spillway","authors":"Thaer Hashem , Jaafar S. Maatooq , Ahmed Y. Mohammed","doi":"10.1016/j.flowmeasinst.2026.103192","DOIUrl":"10.1016/j.flowmeasinst.2026.103192","url":null,"abstract":"<div><div>This study aims to provide an innovative development the traditional steps by creating holes of dimensions, numbers and geometric arrangement to enable the flow to penetrate through and overlaps with the main flow passing over the crest, leading to an increase in turbulence areas responsible for increasing the chance of air interanment, which is meaning more increasing in the dissipation of kinetic energy. This new approach has been tested numerically using CFD and verified experimentally. Different scenarios of upstream flow conditions, and geometric properties of holes, perforation ratio, number of rows, spacing in between, and alignment have been adopted in the test. The results show that a lower perforation ratio, a single straight row, and a single step distance yield the best performance for the aim among all adopted scenarios. Compared to traditional design, the result indicated that the energy dissipation increased to approximately 130 % and the gained evacuated discharge up to 150 % for identical upstream flow conditions. Furthermore, Local pressure distribution reduction up to 85 %. Turbulent intensity, turbulent dissipation, flow field pressure fluctuation, and volume of fluid fraction perform well with decreasing perforation ratio, number of rows, and step distance, with straight alignment. This study will be beneficial for safely removing high excess flow while maintaining a safe downstream environment.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103192"},"PeriodicalIF":2.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.flowmeasinst.2025.103177
Zhansong Xu , Haozhe Jin , Haotian Xu , Chao Wang
The noise characteristics of high-parameter control valves are an important research direction of industrial automation, and in petrochemical, energy and other industries, their noise affects the stability, life and operating environment of equipment. In this paper, the noise characteristics of high-parameter cascade multistage control valves are discussed, the noise generation mechanism, influencing factors and numerical simulation methods are analyzed, and the cavitation noise characteristics are studied by controlling the cavitation number variables under different openings, so as to provide support for noise reduction and optimal design. The results show that the sound pressure level of cavitation noise increases first and then decreases with the increase of opening, reaching 139 dB at 65 % opening and cavitation number at 1, and the noise mainly comes from the sudden change of multi-stage throttling structure and outlet of the cascade spool. At cavitation numbers 1 and 1.2, cavitation bubble collapse intensity is the highest and noise is the highest. The near-field sound pressure level of flow-induced noise is the largest, reaching 192 dB at 65 % opening, showing the broadband characteristics dominated by medium and high frequencies, and the noise decreases and directivity disappears with the increase of the radial distance of the monitoring point.
{"title":"Study on noise characteristics of high-parameter string multi-stage control valve","authors":"Zhansong Xu , Haozhe Jin , Haotian Xu , Chao Wang","doi":"10.1016/j.flowmeasinst.2025.103177","DOIUrl":"10.1016/j.flowmeasinst.2025.103177","url":null,"abstract":"<div><div>The noise characteristics of high-parameter control valves are an important research direction of industrial automation, and in petrochemical, energy and other industries, their noise affects the stability, life and operating environment of equipment. In this paper, the noise characteristics of high-parameter cascade multistage control valves are discussed, the noise generation mechanism, influencing factors and numerical simulation methods are analyzed, and the cavitation noise characteristics are studied by controlling the cavitation number variables under different openings, so as to provide support for noise reduction and optimal design. The results show that the sound pressure level of cavitation noise increases first and then decreases with the increase of opening, reaching 139 dB at 65 % opening and cavitation number at 1, and the noise mainly comes from the sudden change of multi-stage throttling structure and outlet of the cascade spool. At cavitation numbers 1 and 1.2, cavitation bubble collapse intensity is the highest and noise is the highest. The near-field sound pressure level of flow-induced noise is the largest, reaching 192 dB at 65 % opening, showing the broadband characteristics dominated by medium and high frequencies, and the noise decreases and directivity disappears with the increase of the radial distance of the monitoring point.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103177"},"PeriodicalIF":2.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.flowmeasinst.2026.103196
Jiaqi Wen , Haodi Jia , Jiale Li , Lei Gao , Lide Fang , Ning Zhao
<div><div>Gas-liquid two-phase plug flow is widely prevalent in industrial scenarios such as aircraft cooling, chemical production, and falling film evaporation. Conducting precise measurement research on the characteristic parameters of Taylor bubbles within plug flow is a key research focus in the field of two-phase flow. In gas-liquid two-phase plug flow, parameters such as the slug length and the void fraction are crucial for studying the dynamic characteristics of the two phases. The study adopts a visual sensor measurement system utilizing refraction-corrected high-speed imaging technology to achieve dynamic measurement of the characteristic parameters of gas plugs. Based on computer vision technology and integrated with deep learning methodologies, an optimized edge detection operator is employed to accurately locate and extract the contour of the gas plug. Data acquisition and piecewise polynomial fitting modeling are conducted for the characteristic parameters such as the length of the gas plug head, the height of the gas plug head, and the average void fraction of the gas plug. The results demonstrate that for the prediction model of the length-to-height ratio of the gas plug head, at the superficial velocities condition of 0.028< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.283, the mean absolute percentage error (MAPE) of the prediction results is 12.53 %, with over 80 % of prediction results falling within a ±20 % relative error range. At the superficial velocities condition of 0.354< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.495, the MAPE of the prediction results is 14.24 %, with over 80 % of prediction results within a ±20 % relative error range. For the prediction model of the average void fraction of the gas plug, at the superficial velocities condition of 0.028< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.354, the MAPE of the prediction results is 3.96 %, with 99 % of the prediction results within the range of ±15 % relative error. At the superficial velocities condition of 0.424< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.495, the MAPE of the prediction results is 8.26 %, with 83 % of the prediction results within the range of ±15 % relative error. On this basis, the machine learning method is adopted to improve the prediction accuracy the model. A Support <strong>Vector Regression (SVR)</strong> based prediction model for the length-to-height ratio of the gas plug head and the average void fraction of the gas plug is established. The model prediction results show that, the MAPE of the height-to-length ratio of gas plug head prediction model is 4.74 %, while the MAPE of the gas plug average void fraction prediction model is 0.84 %, further improving the generalization ability of the mo
{"title":"Gas plug characteristic parameters measurement based on high-speed imaging technology","authors":"Jiaqi Wen , Haodi Jia , Jiale Li , Lei Gao , Lide Fang , Ning Zhao","doi":"10.1016/j.flowmeasinst.2026.103196","DOIUrl":"10.1016/j.flowmeasinst.2026.103196","url":null,"abstract":"<div><div>Gas-liquid two-phase plug flow is widely prevalent in industrial scenarios such as aircraft cooling, chemical production, and falling film evaporation. Conducting precise measurement research on the characteristic parameters of Taylor bubbles within plug flow is a key research focus in the field of two-phase flow. In gas-liquid two-phase plug flow, parameters such as the slug length and the void fraction are crucial for studying the dynamic characteristics of the two phases. The study adopts a visual sensor measurement system utilizing refraction-corrected high-speed imaging technology to achieve dynamic measurement of the characteristic parameters of gas plugs. Based on computer vision technology and integrated with deep learning methodologies, an optimized edge detection operator is employed to accurately locate and extract the contour of the gas plug. Data acquisition and piecewise polynomial fitting modeling are conducted for the characteristic parameters such as the length of the gas plug head, the height of the gas plug head, and the average void fraction of the gas plug. The results demonstrate that for the prediction model of the length-to-height ratio of the gas plug head, at the superficial velocities condition of 0.028< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.283, the mean absolute percentage error (MAPE) of the prediction results is 12.53 %, with over 80 % of prediction results falling within a ±20 % relative error range. At the superficial velocities condition of 0.354< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.495, the MAPE of the prediction results is 14.24 %, with over 80 % of prediction results within a ±20 % relative error range. For the prediction model of the average void fraction of the gas plug, at the superficial velocities condition of 0.028< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.354, the MAPE of the prediction results is 3.96 %, with 99 % of the prediction results within the range of ±15 % relative error. At the superficial velocities condition of 0.424< <span><math><mrow><msub><mi>u</mi><mrow><mi>s</mi><mi>g</mi></mrow></msub></mrow></math></span> <0.495, the MAPE of the prediction results is 8.26 %, with 83 % of the prediction results within the range of ±15 % relative error. On this basis, the machine learning method is adopted to improve the prediction accuracy the model. A Support <strong>Vector Regression (SVR)</strong> based prediction model for the length-to-height ratio of the gas plug head and the average void fraction of the gas plug is established. The model prediction results show that, the MAPE of the height-to-length ratio of gas plug head prediction model is 4.74 %, while the MAPE of the gas plug average void fraction prediction model is 0.84 %, further improving the generalization ability of the mo","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103196"},"PeriodicalIF":2.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.flowmeasinst.2026.103191
M.A. Sebak , A.K. Aladim , Fatma Gami , Abdalrahman M. Rayan , Mahrous R. Ahmed , Mohamed Asran Hassan
This study details the design, implementation, and validation of a modular, economical instrumentation system for the concurrent assessment of gas-sensing and photoconductivity features in semiconductor materials. The developed platform integrates off-the-shelf components, such as an Arduino-based microcontroller, a high-precision data-logging multimeter, and a time-regulated motorized ball valve, resulting in a cost reduction exceeding 90 % compared to commercial alternatives, while maintaining scientific accuracy. The system includes a bespoke Pyrex glass chamber and a PID-regulated heating element, guaranteeing consistent and replicable environmental conditions for both bulk pellets and thin-film samples. To assess the system's performance, sensing measurements were performed on a CuO pellet subjected to CO2 gas. Experimental findings indicated a maximum sensitivity of 33.68 % at high temperatures, with response and recovery durations recorded at 692 s and 476 s, respectively. Arrhenius analysis produced activation energies of 0.561 eV in the high-temperature domain and 0.161 eV in the low-temperature domain, demonstrating remarkable quantitative concordance with existing literature. Moreover, the integrated optical module, employing a modulated video projector as a multi-wavelength light source, effectively exhibited the system's ability to conduct photo-induced conductivity measurements. This dual-functional architecture offers a dependable, high-throughput option for researchers investigating intricate light-assisted sensing mechanisms, substantially reducing the financial barrier to advanced materials characterization.
{"title":"Design and optimization of a universal, cost-effective gas sensing and photoconductivity measuring system","authors":"M.A. Sebak , A.K. Aladim , Fatma Gami , Abdalrahman M. Rayan , Mahrous R. Ahmed , Mohamed Asran Hassan","doi":"10.1016/j.flowmeasinst.2026.103191","DOIUrl":"10.1016/j.flowmeasinst.2026.103191","url":null,"abstract":"<div><div>This study details the design, implementation, and validation of a modular, economical instrumentation system for the concurrent assessment of gas-sensing and photoconductivity features in semiconductor materials. The developed platform integrates off-the-shelf components, such as an Arduino-based microcontroller, a high-precision data-logging multimeter, and a time-regulated motorized ball valve, resulting in a cost reduction exceeding 90 % compared to commercial alternatives, while maintaining scientific accuracy. The system includes a bespoke Pyrex glass chamber and a PID-regulated heating element, guaranteeing consistent and replicable environmental conditions for both bulk pellets and thin-film samples. To assess the system's performance, sensing measurements were performed on a CuO pellet subjected to CO<sub>2</sub> gas. Experimental findings indicated a maximum sensitivity of 33.68 % at high temperatures, with response and recovery durations recorded at 692 s and 476 s, respectively. Arrhenius analysis produced activation energies of 0.561 eV in the high-temperature domain and 0.161 eV in the low-temperature domain, demonstrating remarkable quantitative concordance with existing literature. Moreover, the integrated optical module, employing a modulated video projector as a multi-wavelength light source, effectively exhibited the system's ability to conduct photo-induced conductivity measurements. This dual-functional architecture offers a dependable, high-throughput option for researchers investigating intricate light-assisted sensing mechanisms, substantially reducing the financial barrier to advanced materials characterization.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103191"},"PeriodicalIF":2.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.flowmeasinst.2026.103188
Zihan Lu , Libing Zhang , Yudi Zhu , Xinzhi Zhou , Hailin Wang , Jialiang Zhu
The reactor coolant flow of the primary loop is one of the key thermal-hydraulic parameters in nuclear reactor operation, and its measurement accuracy directly relates to the safety and stability of the plant. In pressurized water reactor (PWR) nuclear power plants, elbow flow meters are typically used to measure the coolant flow. However, the transition section of the primary loop pipe contains a non-uniform, highly dynamic flow-heat coupling field, leading to certain uncertainties in coolant flow measurement. To address the complex operating conditions at the transition section of the steam generator outlet, this study constructs an orthogonal curvilinear coordinate system adapted to the elbow geometry and, based on tensor analysis and the Navier–Stokes equations, derives a Radial Pressure Gradient Equation (RPGE). A mechanistic analytical framework is established to identify and decompose the sources of wall pressure difference in elbows and to evaluate their contributions to measurement uncertainty quantitatively. CFD numerical simulations are further conducted to validate the applicability and computational accuracy of the proposed model. Results indicate that under the uniform-density flow assumption, discrepancies between RPGE predictions and CFD results remain within 0.15 % across all investigated operating conditions. Source-term decomposition reveals that the convection term and the primary flow term constitute the dominant contributors to uncertainty, each accounting for approximately 11 % of the wall pressure difference. Nonetheless, these two contributions partially cancel each other numerically, resulting in a total uncertainty consistently maintained at less than 1 %. Under variable-density flow conditions, discrepancies between RPGE and CFD results remain within 2 %. The primary impact of the non-uniform temperature field is the increased dispersion of the quantified uncertainty intervals of individual source terms. Compared with the conservative empirical uncertainty range typically adopted in engineering practice (−3 %–3 %), the uncertainty intervals derived from the RPGE framework are reduced by approximately 35 %–40 % on average across different operating conditions. The proposed analytical approach provides an interpretable theoretical basis and a systematic quantitative tool for tracing and evaluating uncertainty in elbow flow meters operating under complex flow–heat coupling environments.
{"title":"Study on the mechanism of flow measurement uncertainty in elbow flow meters under complex flow-heat coupling conditions","authors":"Zihan Lu , Libing Zhang , Yudi Zhu , Xinzhi Zhou , Hailin Wang , Jialiang Zhu","doi":"10.1016/j.flowmeasinst.2026.103188","DOIUrl":"10.1016/j.flowmeasinst.2026.103188","url":null,"abstract":"<div><div>The reactor coolant flow of the primary loop is one of the key thermal-hydraulic parameters in nuclear reactor operation, and its measurement accuracy directly relates to the safety and stability of the plant. In pressurized water reactor (PWR) nuclear power plants, elbow flow meters are typically used to measure the coolant flow. However, the transition section of the primary loop pipe contains a non-uniform, highly dynamic flow-heat coupling field, leading to certain uncertainties in coolant flow measurement. To address the complex operating conditions at the transition section of the steam generator outlet, this study constructs an orthogonal curvilinear coordinate system adapted to the elbow geometry and, based on tensor analysis and the Navier–Stokes equations, derives a Radial Pressure Gradient Equation (RPGE). A mechanistic analytical framework is established to identify and decompose the sources of wall pressure difference in elbows and to evaluate their contributions to measurement uncertainty quantitatively. CFD numerical simulations are further conducted to validate the applicability and computational accuracy of the proposed model. Results indicate that under the uniform-density flow assumption, discrepancies between RPGE predictions and CFD results remain within 0.15 % across all investigated operating conditions. Source-term decomposition reveals that the convection term and the primary flow term constitute the dominant contributors to uncertainty, each accounting for approximately 11 % of the wall pressure difference. Nonetheless, these two contributions partially cancel each other numerically, resulting in a total uncertainty consistently maintained at less than 1 %. Under variable-density flow conditions, discrepancies between RPGE and CFD results remain within 2 %. The primary impact of the non-uniform temperature field is the increased dispersion of the quantified uncertainty intervals of individual source terms. Compared with the conservative empirical uncertainty range typically adopted in engineering practice (−3 %–3 %), the uncertainty intervals derived from the RPGE framework are reduced by approximately 35 %–40 % on average across different operating conditions. The proposed analytical approach provides an interpretable theoretical basis and a systematic quantitative tool for tracing and evaluating uncertainty in elbow flow meters operating under complex flow–heat coupling environments.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103188"},"PeriodicalIF":2.7,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-07DOI: 10.1016/j.flowmeasinst.2026.103187
Ameer Ali Shaalan, Wafa Mefteh, Ali Mohsen Frihida
Effective and efficient fault diagnosis in triplex pumps is critical to maintaining safety and uptime in important applications such as oil and gas, chemicals, and mining. This paper presents a novel data-driven approach based on the Continuous Wavelet Transform (CWT) and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the identification of faults due to flow signals. The CWT is then used to extract time–frequency features from raw flow time series and to detect the transient and non-stationary patterns that may correspond to specific fault types. The extracted features are then passed to a Bidirectional LSTM (BiLSTM) network to learn the bidirectional temporal dependency. A dataset with 1000 labelled flow samples with different fault conditions, such as bearing wear, leakage, and multi-faults, is used for model training and testing. The model achieved a validation accuracy of up to 94 %, indicating its strong performance in single and combined fault classification. The confusion matrix analysis shows a good classification performance; cosine-confusion was found in conflicting cases that contain multiple faults. This work demonstrates that fusing TF analysis with Deep Learning architecture can provide more accurate predictions of time to failure in industrial pump systems and can enhance predictive maintenance techniques.
{"title":"A hybrid CWT–BiLSTM framework for accurate fault diagnosis in triplex pumps under complex operating conditions","authors":"Ameer Ali Shaalan, Wafa Mefteh, Ali Mohsen Frihida","doi":"10.1016/j.flowmeasinst.2026.103187","DOIUrl":"10.1016/j.flowmeasinst.2026.103187","url":null,"abstract":"<div><div>Effective and efficient fault diagnosis in triplex pumps is critical to maintaining safety and uptime in important applications such as oil and gas, chemicals, and mining. This paper presents a novel data-driven approach based on the Continuous Wavelet Transform (CWT) and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the identification of faults due to flow signals. The CWT is then used to extract time–frequency features from raw flow time series and to detect the transient and non-stationary patterns that may correspond to specific fault types. The extracted features are then passed to a Bidirectional LSTM (BiLSTM) network to learn the bidirectional temporal dependency. A dataset with 1000 labelled flow samples with different fault conditions, such as bearing wear, leakage, and multi-faults, is used for model training and testing. The model achieved a validation accuracy of up to 94 %, indicating its strong performance in single and combined fault classification. The confusion matrix analysis shows a good classification performance; cosine-confusion was found in conflicting cases that contain multiple faults. This work demonstrates that fusing TF analysis with Deep Learning architecture can provide more accurate predictions of time to failure in industrial pump systems and can enhance predictive maintenance techniques.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103187"},"PeriodicalIF":2.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-07DOI: 10.1016/j.flowmeasinst.2026.103195
Meng Wang , Ruowei Li , Yanyan Shi , Zhen Yang , Manuchehr Soleimani
Flow regime recognition is very important in the two-phase flow measurement. However, considering that two-phase flow is much more complicated than single-phase flow, flow regime cannot be accurately identified by mechanism model. In this work, a novel intelligent data-driven method based on image encoding and transformer is proposed to recognize typical flow regimes encountered in the horizontal air-water flow. Dynamic experiment is carried out using a ring-shaped conductance sensor to collect voltage signals of bubble flow, bubble-slug flow, slug flow, slug-stratified flow and stratified flow. To highlight the characteristic differences between different flow regimes, the measured signals are encoded into two-dimensional images. To classify the encoded images of the five flow regimes, transformer models are then established. With the encoded images as the input of the model, flow regime identification is implemented by training of the model. The results demonstrate that the characteristics of different flow regimes can be better reflected in the encoded image with Gramian angular field. Meanwhile, the recognition accuracy of Swin Transformer is advantageous to that of Vision Transformer in the classification of the encoded images of the five flow regimes. Comparing with other identification methods, the method which combines Gramian angular field with Swin Transformer shows the best performance in the recognition of the flow regimes. The total accuracy reaches as high as 99.1 % This study offers an alternative for accurate flow regime recognition in two-phase flow measurement.
{"title":"An intelligent data-driven flow regime recognition method for horizontal air-water two-phase flow","authors":"Meng Wang , Ruowei Li , Yanyan Shi , Zhen Yang , Manuchehr Soleimani","doi":"10.1016/j.flowmeasinst.2026.103195","DOIUrl":"10.1016/j.flowmeasinst.2026.103195","url":null,"abstract":"<div><div>Flow regime recognition is very important in the two-phase flow measurement. However, considering that two-phase flow is much more complicated than single-phase flow, flow regime cannot be accurately identified by mechanism model. In this work, a novel intelligent data-driven method based on image encoding and transformer is proposed to recognize typical flow regimes encountered in the horizontal air-water flow. Dynamic experiment is carried out using a ring-shaped conductance sensor to collect voltage signals of bubble flow, bubble-slug flow, slug flow, slug-stratified flow and stratified flow. To highlight the characteristic differences between different flow regimes, the measured signals are encoded into two-dimensional images. To classify the encoded images of the five flow regimes, transformer models are then established. With the encoded images as the input of the model, flow regime identification is implemented by training of the model. The results demonstrate that the characteristics of different flow regimes can be better reflected in the encoded image with Gramian angular field. Meanwhile, the recognition accuracy of Swin Transformer is advantageous to that of Vision Transformer in the classification of the encoded images of the five flow regimes. Comparing with other identification methods, the method which combines Gramian angular field with Swin Transformer shows the best performance in the recognition of the flow regimes. The total accuracy reaches as high as 99.1 % This study offers an alternative for accurate flow regime recognition in two-phase flow measurement.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"108 ","pages":"Article 103195"},"PeriodicalIF":2.7,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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