Flow Measurement and Instrumentation最新文献

{"title":"A hybrid cellular automata-based topology optimization method for incompressible fluid flow channels","authors":"Wang Guan ,&nbsp;Guangfei Xu ,&nbsp;Linyuan Kou ,&nbsp;Wang Li ,&nbsp;Hongping Liu ,&nbsp;Le Xu ,&nbsp;Xu Wang","doi":"10.1016/j.flowmeasinst.2025.102867","DOIUrl":"10.1016/j.flowmeasinst.2025.102867","url":null,"abstract":"<div><div>Optimization of fluid flow paths has become essential for enhancing the performance and efficiency of engineering systems. This study investigates the topology optimization problem for steady-state incompressible Navier-Stokes flow. By leveraging the potential of hybrid cellular automata to simulate complex system behaviors, this approach is combined with computational fluid dynamics to propose a variable density topology optimization algorithm applicable to incompressible fluid flow channels. Using a three-terminal device as an example, numerical simulations and experiments verify that the variable density topology optimization method for the Solid Isotropic Material with Penalization interpolation model, when augmented with a local control criterion, significantly improves the performance of the fluid system. The topology optimization system is applied to models under various working conditions, demonstrating good generality.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102867"},"PeriodicalIF":2.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714823","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}

{"title":"Transition of super-to subcritical flow without a hydraulic jump by a cross-sectional transition structure","authors":"Marzieh Rezashahreza,&nbsp;Abdorreza Kabiri-Samani,&nbsp;Mostafa Fazeli","doi":"10.1016/j.flowmeasinst.2025.102889","DOIUrl":"10.1016/j.flowmeasinst.2025.102889","url":null,"abstract":"<div><div>Shock waves complicate the design of hydraulic structures, involving the transitions in a channel with supercritical flow regime. Abrupt changes in characteristics of a supercritical flow, e.g., in convergent transitions, would result in a rapidly varied flow associated with a hydraulic jump, causing waves and surface fluctuations. Therefore, by applying an appropriate transition structure inside the channel, the hydraulic jump can be eliminated, and these disruptions are minimized. In the present study, an analytical/experimental investigation is conducted to design a cross-sectional transition structure CSTS, eliminating the hydraulic jump with supercritical inflow Froude numbers between 2.5 and 5.5. An analytical study was performed to design the transition structures based on the fluid shock waves theory, resulting in a conceptual design diagram for excluding the hydraulic jump from super-to subcritical flow regime. According to the results, the present CSTSs are more efficient than the corresponding bed transition structures BTSs, indicating great potential of the CSTSs to change the flow regime from super-to subcritical flow without a hydraulic jump. Flow inside the CSTSs was analyzed and the analytical results were verified, applying the present experimental measurements. Based on experimental observations, weak vortices are generated downstream of the critical section, disappearing after a fall in the water free-surface profile across the critical section.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102889"},"PeriodicalIF":2.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686170","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}

{"title":"An instance mask representation for bubble size distribution in two-phase bubble flotation column based on deep learning model","authors":"Zhiping Wen ,&nbsp;Maiqiang Zhou ,&nbsp;Sanja Mišković ,&nbsp;Changchun Zhou","doi":"10.1016/j.flowmeasinst.2025.102892","DOIUrl":"10.1016/j.flowmeasinst.2025.102892","url":null,"abstract":"<div><div>Measuring bubble size distribution from images sourced from various two-phase bubble systems presents a significant challenge, yet it holds substantial interest for many researchers in the field. This study introduces a novel approach by leveraging instance segmentation techniques based on deep learning to automatically quantify the bubble size distribution. The effective Mask RCNN and SOLO v2 were used as the basic model structure. The findings reveal that models employing the ResNet-FPN backbone outperform those using ResNet-C4/DC5 backbones in bubble segmentation. Specifically, the Mask RCNN with ResNet 101-FPN backbone achieved an average precision (AP) of 71.34 % for IoU = 0.50 and 68.13 % for IoU = 0.75. In terms of inference time, the SOLO v2 model displayed superior efficiency, taking 0.57 s per image compared to the Mask RCNN model. The study successfully demonstrated the utilization of the least squares fitting method to effectively detect and calculate bubble size distribution.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102892"},"PeriodicalIF":2.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685661","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}

{"title":"Yttria-stabilized zirconia flow sensor for high-temperature and humidity with compensation algorithm","authors":"Huiying Sun ,&nbsp;Yongping Zhang ,&nbsp;Yang Wang ,&nbsp;Wentian Wang ,&nbsp;Lei Zhao ,&nbsp;Xinxin Yan ,&nbsp;Jie Shen ,&nbsp;Junliang Li ,&nbsp;Jie Zou ,&nbsp;Jiawen Jian","doi":"10.1016/j.flowmeasinst.2025.102894","DOIUrl":"10.1016/j.flowmeasinst.2025.102894","url":null,"abstract":"<div><div>Flow sensors are widely used in industrial production, but their measurement accuracy remains insufficient in high-temperature and high-humidity environments. To address this issue, this study developed a novel flow sensor based on yttria-stabilized zirconia (YSZ) solid-state electrolytes. It integrates both flow velocity and humidity measurement capabilities. This research systematically investigated the effects of environmental temperature and humidity on the sensor's output signals and proposed a compensation algorithm to mitigate temperature and humidity interference, thereby enhancing measurement accuracy. Experimental results showed that the sensor output signal decreases with increasing inlet temperature and increases with rising inlet humidity. Through linear fitting, the relationship between flow velocity output signal drift and environmental temperature and humidity was established. This relationship was then utilized for compensation, resulting in a significant improvement in measurement accuracy. The experiments demonstrated that the sensor exhibits high accuracy and stability in high-temperature and high-humidity environments, effectively optimizing flow velocity detection performance.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102894"},"PeriodicalIF":2.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726018","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}

{"title":"Research on dynamic characteristics of a novel high-frequency water hydraulic proportional flow valve","authors":"Hao Wang ,&nbsp;Chao Cao ,&nbsp;Jiyun Zhao ,&nbsp;Mingquan Yu ,&nbsp;Yiye Zhang","doi":"10.1016/j.flowmeasinst.2025.102893","DOIUrl":"10.1016/j.flowmeasinst.2025.102893","url":null,"abstract":"<div><div>Accurately measuring the dynamic characteristics of high-pressure large-flow water hydraulic flow valves and improving their frequency response are critical challenges in water hydraulic systems. In this study, a high-pressure large-flow and high-frequency water hydraulic proportional flow valve (WHPFV) integrated an indirect displacement-based measurement method (IDMM) is proposed. The main spool employs a symmetrical cylinder cone structure, featuring a non-full circle U-shaped valve port. A low-pressure small-flow oil hydraulic servo valve is used to control the high-pressure and large-flow water hydraulic main spool. A couple mathematical model about the displacement and output flow of the main valve, as well as a main spool displacement transfer function model, are established. The effect of various parameters on the dynamic characteristics of the flow valve is studied by simulation. To demonstrate the superiority of IDMM, different dynamic behaviors of WHPFV are measured. Experimental results indicate that the WHPFV exhibits excellent dynamic characteristics with the rise time about 30 ms, and the IDMM is able to measure the dynamic performance of WHPFV effectively.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102893"},"PeriodicalIF":2.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685660","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}

{"title":"Computer vision techniques for Taylor bubble detection and velocity measurement using YOLO v8 and optical flow","authors":"Leonardo Fadel Metzker,&nbsp;Cáio César Silva Araújo,&nbsp;Maurício de Melo Freire Figueiredo,&nbsp;Ana Maria Frattini Fileti","doi":"10.1016/j.flowmeasinst.2025.102885","DOIUrl":"10.1016/j.flowmeasinst.2025.102885","url":null,"abstract":"<div><div>This study focuses on measuring the velocity and length of Taylor bubbles in vertical liquid–gas two-phase slug flows using a non-intrusive image-based methodology. High-resolution video footage of the flow was analyzed through the YOLO v8 object detection algorithm, combined with the optical flow technique for motion analysis. The proposed method achieved an average detection precision of 84.3% and a velocity measurement accuracy of 88%. Measurements of bubble lengths showed maximum deviations of 1.1% when compared to manual measurements, with larger discrepancies occurring primarily in cases involving elongated or deformed bubbles. Velocity measurements demonstrated strong agreement with mechanistic model predictions, with deviations not exceeding 6%. These results highlight the robustness and reliability of the proposed method for analyzing bubble dynamics, offering valuable insights into flow behavior for multiphase flow applications and facilitating improved process optimization.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102885"},"PeriodicalIF":2.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686204","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}

{"title":"Study on the dynamic characteristics of CDC valves based on a thermo-fluid-solid coupling model","authors":"Yumeng Li ,&nbsp;Fangwei Xie ,&nbsp;Jian Lu ,&nbsp;Anxin Sun ,&nbsp;Zuzhi Tian ,&nbsp;Shuyou Wang","doi":"10.1016/j.flowmeasinst.2025.102895","DOIUrl":"10.1016/j.flowmeasinst.2025.102895","url":null,"abstract":"<div><div>CDC valve is a multi-physics coupling system, where flow field simulation accuracy is crucial for dynamic characteristic analysis. This study develops a CFD-based thermo-fluid-structure coupling model, integrating interactions among flow, thermal, and stress fields, with experimental validation of its accuracy. Results show that the CDC valve reaches peak flow at 50 °C but exhibits a declining trend at higher temperatures. Under 30 °C and 6 MPa conditions, the maximum error between experimental and simulated flow rates is 7.46 %, demonstrating model reliability. This research reveals the dynamic behavior of the CDC valve under high-temperature and high-pressure conditions, providing theoretical support for optimized design and expanding multi-physics modeling of complex hydraulic components.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102895"},"PeriodicalIF":2.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685659","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}

{"title":"Study on the flow field and steady-state hydrodynamics of a sliding valve with a sloping U-shaped throttle groove","authors":"Shipeng Shangguan ,&nbsp;Huawei Duan ,&nbsp;Ruichuan Li ,&nbsp;Wentao Yuan ,&nbsp;Lanzheng Chen ,&nbsp;Zhibo Wang ,&nbsp;Hui Chen","doi":"10.1016/j.flowmeasinst.2025.102886","DOIUrl":"10.1016/j.flowmeasinst.2025.102886","url":null,"abstract":"<div><div>Existing U-shaped throttle grooves in spool valves face issues such as unstable fluid flow, high-pressure drop, and uneven variation of flow area. This paper proposes a novel spool valve throttle groove featuring a sloped U-shaped configuration. Through theoretical derivation of mathematical models for flow areas of different throttle grooves combined with CFD simulation analysis of flow field characteristics and distribution patterns, the study reveals the influence of flow area change gradient on hydrodynamic forces. Furthermore, three-dimensional hydraulic spool valve models with different throttle grooves were established to analyze the effects of key parameters - valve opening (1 mm, 3 mm, 5 mm), pressure difference (1 MPa, 3 MPa, 5 MPa), and slope angle (6°, 12°, 18°) - on flow field characteristics and hydrodynamic behavior. Results demonstrate that the sloped U-shaped throttle groove exhibits more stable pressure and velocity distributions under varying openings and pressure differences, significantly reducing hydrodynamic force fluctuations with a maximum reduction of 59.72 %, thereby improving system stability and flow control accuracy. Experimental validation using a comprehensive hydraulic valve test bench confirmed the accuracy of the spool valve simulation model. The research indicates that the sloped U-shaped throttle groove effectively enhances spool valve system stability, extends component lifespan, and improves hydraulic control precision, providing theoretical support for designing and optimizing hydraulic spool valves.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102886"},"PeriodicalIF":2.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631928","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}

{"title":"Investigation of particle reconstruction quality for three-dimensional light field PIV","authors":"Xiaoyu Zhu ,&nbsp;Jiaxing Lu ,&nbsp;Md Moinul Hossain ,&nbsp;Chuanlong Xu","doi":"10.1016/j.flowmeasinst.2025.102888","DOIUrl":"10.1016/j.flowmeasinst.2025.102888","url":null,"abstract":"<div><div>This study presents a comprehensive investigation into the reconstruction quality factor, a critical metric for assessing particle position reconstruction accuracy in light field particle image velocimetry (LF-PIV). Key factors influencing the reconstruction quality are analyzed, and a benchmark criterion for reconstruction quality is proposed to ensure high-accuracy three-dimensional flow measurement. Numerical reconstructions of random particle and 3D displacement fields are performed to optimize the tomographic and deep learning reconstruction approaches. Strategies for generating optimal datasets for deep learning models are presented. The findings indicate that the generation of ghost particles and the omission of true particles are the primary causes of low reconstruction quality. The latter has a more noticeable impact, particularly when ghost particle intensities are significantly lower than true particles. A reconstruction quality factor of above 0.7 is recommended for reliable, high-accuracy flow measurements. Learning-based methods outperform tomographic algorithms in particle reconstruction, achieving comparable reconstruction accuracy with a single light field camera (LFC) to that of tomographic methods using dual LFCs. To generate high-quality datasets for deep learning, an optimal angular separation of 0.01° between sampling rays, a seeding density range of 0∼1 particle per microlens, and variable particle peak intensities are suggested. Additionally, incorporating noise at 10 % of the image intensity standard deviation into training data significantly enhances model robustness.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102888"},"PeriodicalIF":2.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631926","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}

{"title":"Comparative prediction of pressure and velocity in 3D flow field based on neural networks","authors":"Xiumei Liu,&nbsp;Su Wu,&nbsp;Beibei Li,&nbsp;Rui Han,&nbsp;Linmin Xu","doi":"10.1016/j.flowmeasinst.2025.102890","DOIUrl":"10.1016/j.flowmeasinst.2025.102890","url":null,"abstract":"<div><div>As an important component in the coal liquefaction system, the regulating valve's flow field and pressure distribution affects the service life and working stability of the system. In order to achieve rapid prediction of the three-dimensional(3D) pressure and velocity in the regulating valve, a prediction model based on neural network was built. The hyperparameters of the model were selected and the network parameters were optimized through genetic algorithms. The training model was verified under different working conditions. The value of axial velocity and radial velocity predicted by the optimized GA-BP model are discussed. The predicted axial velocity <em>v</em>_<em>x</em>, radial velocity <em>v</em>_<em>y</em> and <em>v</em>_<em>z</em> are almost the same with the simulation results. The largest velocity located near the orifice because of the sudden decreasing flow area, and there is a local low speed area near the head of the core head. And the distribution of pressure in the valve is also predicted by this proposed GA-BP model. There is a reflux with local low pressure is located near the orifice, and the error between the simulation and predicted results is about 2 %. Furthermore, the 3D flow field in the regulating valve with higher working pressure is predicted which cannot be easily measured experimentally. The value of resultant velocity <span><math><mrow><mover><mi>v</mi><mo>‾</mo></mover></mrow></math></span> is close to the axial velocity <span><math><mrow><msub><mover><mi>v</mi><mo>‾</mo></mover><mi>x</mi></msub></mrow></math></span>, the maximum value of <span><math><mrow><msub><mover><mi>v</mi><mo>‾</mo></mover><mi>x</mi></msub></mrow></math></span> is about 200 m.s⁻¹ which is located near the orifice. The value of radial velocity <span><math><mrow><mo>|</mo><msub><mover><mi>v</mi><mo>‾</mo></mover><mi>y</mi></msub><mo>|</mo></mrow></math></span> and <span><math><mrow><mo>|</mo><msub><mover><mi>v</mi><mo>‾</mo></mover><mi>z</mi></msub><mo>|</mo></mrow></math></span> are almost the same, because the structure of the experimental valve is axisymmetric. The maximum value of <span><math><mrow><mo>|</mo><msub><mover><mi>v</mi><mo>‾</mo></mover><mi>y</mi></msub><mo>|</mo></mrow></math></span> and <span><math><mrow><mo>|</mo><msub><mover><mi>v</mi><mo>‾</mo></mover><mi>z</mi></msub><mo>|</mo></mrow></math></span> are 38.3 m.s⁻¹ and 36.2 m.s⁻¹ respectively. This GA-BP prediction model has a good learning effect on the characteristics of the flow field in the regulating valve, could reflect and predict the operation status of the system.</div></div>","PeriodicalId":50440,"journal":{"name":"Flow Measurement and Instrumentation","volume":"104 ","pages":"Article 102890"},"PeriodicalIF":2.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632029","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}