Measurement最新文献_第4页

Development of a gas-liquid flow measurement method based on centrifugal differential pressure

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-27 DOI: 10.1016/j.measurement.2025.117127

Zhihui Wang , Xingkai Zhang , Jianwei Wang , Kai Guo , Weixia Yang , Peng Zhang

Natural gas wellhead flow and water cut data are essential for enabling real-time dynamic analysis and intelligent parameterization of gas wells. This study investigates the interaction between differential pressure and variables such as water cut, superficial velocity, temperature, and pressure within a centrifugal field, employing multiphase flow theory, finite element analysis (FEA), and laboratory data. It examines the relationship between the unsteady distribution of gas–liquid phases in the centrifugal field and the resulting static pressure profile. Key findings reveal that the gas superficial velocity is directly proportional to both axial differential pressure (ADP) and radial differential pressure (RDP), while inversely related to the differential pressure ratio. In the centrifugal field, the gas and liquid phases form a symmetrical distribution around the central axis, characterized by a “gas core + liquid ring” structure. The static pressure curve displays a distinct inflection at the gas–liquid interface, with higher pressure within the liquid phase and lower pressure in the gas phase, consistent with established principles. The RDP, a consequence of this organized gas–liquid distribution, reflects the cross-sectional water cut, and an inverse water cut measurement model demonstrates an impressive prediction accuracy of 97.2 %. Additionally, the gas–liquid two-phase friction coefficient is rederived from the gas friction multiplier, incorporating the effects of temperature and pressure on gas density. Ultimately, a novel gas–liquid two-phase flow measurement model for high gas–liquid ratios is developed, achieving an average relative error of just 7.18 %. This model satisfies the rigorous accuracy requirements for water cut monitoring at gas wellheads and offers critical theoretical support for the measurement of multiphase flow.

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引用次数: 0

Specific absorption rate analysis for Mitigation of electromagnetic hazards in laboratory environment using a miniaturized antenna at 2.45 GHz

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-27 DOI: 10.1016/j.measurement.2025.117135

Aijaz Ahmed , Usha Keshwala , Vineeta Kumari , Satya Kesh Dubey

Specific Absorption Rate (SAR) is a critical parameter that is used to define the maximum radiation characteristic of a wireless device operating at RF frequencies. IEEE and ICNIRP have given the guidelines of limiting the exposure for electromagnetic (EM) radiation to protect the human’s health (i. e. SAR of mobile is defined as 1.6 W/Kg and the general exposure limit is set to 2 W/Kg). This work emphasizes on the analysis of SAR measurement at multiple exposure time for different wireless devices including mobile phones, Wi-Fi routers, and waveguide antennas at 2.45 GHz. For development of the methodology, a miniaturized antenna is designed using Golden ratio and Fibonacci sequence that gives resonance for fundamental mode at 2.45 GHz frequency and separates out all the higher mode resonances. The antenna achieves peak radiation gain of 2.37 dBi in the frequency range of 2.12–2.72 GHz that comes under industrial, scientific and medical bands. The performance of the proposed antenna is validated for SAR measurement by comparing the outcome measured with a standard E-Field probe. These findings underscore the importance of monitoring electromagnetic exposure to safeguard both laboratory equipment and the health of researchers.

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引用次数: 0

Dynamic recognition of coal-rock interface based on hardness characteristic preference and multisensor information fusion

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-27 DOI: 10.1016/j.measurement.2025.117139

Haijian Wang, Han Mo, Xingrui Fan, Zhouxiang Hu

A multisensor information fusion method for coal-rock interface recognition based on hardness characteristic preference was proposed to overcome the impact of hardness differences between coal and rock on the accuracy of coal-rock interface recognition during the mining process. First, a coal-rock cutting experimental platform was established using 15 coal-rock specimens with three hardness characteristics (soft coal-hard rock, coal-rock with similar hardness, and hard coal-soft rock) and five coal-rock ratios. Then, cutting experiments were conducted, and frequency-domain analysis coupled with wavelet packet reconstruction was employed to construct a multicutting signal characteristic value database encompassing the current and triaxial vibration signals. Subsequently, membership functions for multicutting characteristic signals were developed based on minimum fuzziness principles under varying hardness conditions, with membership degree thresholds optimized via a particle swarm optimization (PSO) algorithm. Finally, a coal-rock interface recognition decision model was constructed by integrating the Dempster–Shafer (D-S) evidence theory with a multi-PSO framework. The experimental results demonstrate that the proposed method achieves a maximum recognition accuracy of 0.9626 for specimens with diverse hardness characteristics, reduces the uncertainty probability by up to 109.1 %, and yields a total error of 2.38 % (55.93 % reduction) in coal residue and rock intrusion scenarios. The approach provides a robust theoretical foundation and technical framework for advancing intelligent coal-mining systems.

为克服开采过程中煤与岩石硬度差异对煤岩界面识别精度的影响，提出了一种基于硬度特征偏好的多传感器信息融合的煤岩界面识别方法。首先，利用三种硬度特征（软煤-硬岩、硬度相近的煤-岩、硬煤-软岩）和五种煤-岩配比的 15 块煤-岩试样建立了煤-岩切割实验平台。然后，进行了切割实验，并采用频域分析和小波包重构技术构建了包含电流信号和三轴振动信号的多切割信号特征值数据库。随后，在不同硬度条件下，根据最小模糊性原则开发了多切特征信号的成员函数，并通过粒子群优化（PSO）算法优化了成员度阈值。最后，通过将 Dempster-Shafer (D-S) 证据理论与多粒子群优化框架相结合，构建了煤岩界面识别决策模型。实验结果表明，所提出的方法对具有不同硬度特征的试样实现了 0.9626 的最高识别精度，将不确定性概率降低了 109.1 %，在煤渣和岩石侵入情况下产生的总误差为 2.38 %（降低了 55.93 %）。该方法为推进智能采煤系统提供了坚实的理论基础和技术框架。

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引用次数: 0

Deep learning approach to prediction of drill-bit torque in directional drilling sliding mode: Energy saving

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-27 DOI: 10.1016/j.measurement.2025.117144

Wanpeng CAO , Danyang MEI , Yongmei GUO , Hamzeh Ghorbani

Directional drilling, a sophisticated well-drilling technique, enables precise wellbore navigation toward inaccessible reservoirs via vertical wells while optimizing hydrocarbon recovery and minimizing environmental impact. This method encounters significant challenges in managing torque and drag, particularly during sliding mode, where the drill string remains stationary while the bit rotates, causing unpredictable torque fluctuations. Traditional torque measurement methods, relying on downhole sensors, are often costly and complex. This research examines advanced machine learning (ML) models that leverage commonly available drilling data to predict drill-bit torque during the sliding mode, thus removing the reliance on expensive sensors. The study presents an innovative approach using Deep Auto-Regressive Network (DARN) and Deep Neural Network (DNN) models, specifically designed to predict torque based on directional drilling parameters like Weight on Bit (WOB), Revolutions Per Minute (RPM), and Standpipe Pressure. Using a dataset of 2,746 data rows from four directionally drilled wells in a Middle Eastern oil field, encompassing scenarios such as casing milling, opening the sidetrack drilling window, and navigating various trajectory sections with different build rates and hold intervals to predict drill-bit torque (TQ), these models were trained and evaluated against Support Vector Machine (SVM) and Decision Tree (DT) benchmarks. Results indicate that DARN achieved superior accuracy with an RMSE of 49.6 and an R² of 0.9986, outperforming other models due to its ability to capture complex temporal dependencies. This predictive model facilitates real-time, cost-effective torque management, significantly enhancing operational efficiency in sliding mode directional drilling.

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引用次数: 0

Accurate dimensional characterization of the textured inner surface of the bearing bushing by the mean of a new measurement instrument

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-26 DOI: 10.1016/j.measurement.2025.117068

Tomasz Żochowski , Artur Olszewski , Michel Fillon , Lidia Gałda , Jan Smykla

The measurement of dimples created on the inner bushing surface is a key step in the quality assessment of the textured bearings, because of their substantial effect on the bearing performance. The aim of this study was the complex analysis of the surface topography of textured inner bushing surface with application of the newly designed and built instrument for 360 degrees surface topography measurements. In the article the theoretical models of different textures of the inner bushing surface were analyzed. The influence of spherical dimples: depth, diameter, surface, volume and coverage on the important geometrical parameters describing journal bearings that effect on bearing performance significantly was presented. The effective bearing clearance of textured journal bearing obtained from the model and experiment were in good agreement and differed insignificantly. The differences between values of the theoretical and measured parameters of dimples were very small − approximately 1.2% for dimple depth and 2.6% for dimple volume. The main advantage of the new instrument is the capability of the accurate and precise measurement of inner textured surface of bushing without limitation to just selected zones and without the necessity for destructive testing.

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引用次数: 0

Two-stage bridge point cloud segmentation by fusing deep learning and heuristic methods

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-26 DOI: 10.1016/j.measurement.2025.117125

Tian Zhang , Haonan Chen , Pengfei Li , Haijiang Li

The point cloud acquired from the data acquisition equipment is not segmented by components, and reverse engineering without component segmentation has limited value. Existing heuristic methods achieve high segmentation accuracy but are computationally intensive. On the other hand, deep learning models are quick but often lack accuracy and depend on limited datasets. To address these issues, this paper introduces a fusion method that utilizes results trained on an easily created synthetic dataset to initially segment the point cloud roughly, aiming for a segmentation accuracy and intersection over union ratio of 80 % and 70 %, respectively. Subsequently, a streamlined heuristic method is applied to comprehensively segment the point cloud. The verification results of the instance indicate that this approach achieves the same high level of accuracy (≥99 %) as heuristic methods but increases the speed of segmentation by approximately 2.52 times. The method involves using a synthetic dataset, derived from real point cloud data, in conjunction with the fusion method and selecting a segmentation network that is optimized for simple synthetic datasets.

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引用次数: 0

A radial section morphology reconstruction method for high-temperature forgings based on mirror mapping of non-view domain 基于非视域镜像映射的高温锻件径向截面形态重建方法

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-26 DOI: 10.1016/j.measurement.2025.117041

Qiwen Zhou , Bin Liu , Mu Li , Jie Gao , Rui Zhu , Tao Kong , Yungang Zhang

In forging process, reconstructing the radial section morphology (RSM) of high-temperature forgings is essential to ensuring their quality. However, it is usually achieved by introducing multiple devices or rotating forgings, making measurements complicated and inaccurate. To address these problems, we propose a method for reconstructing the radial section morphology of forgings by combining point clouds collected from the direct-view domain and non-view domain. Firstly, the point clouds are captured by one laser scanner combined with mirror mapping. We introduce statistical filtering and regression analysis that enable removal of discrete points and outlier clusters. Next, the cubic B-spline interpolation filtering algorithm is used to fill in missing data. Our experiments demonstrate accurate reconstruction of RSM of ring forgings having diameters of 299.2 mm with an error of less than 1.5 mm. We also demonstrate the feasibility for reconstructing the profile of non-circular forgings.

引用次数: 0

Enhanced defect detection on steel surfaces using integrated residual refinement module with synthetic data augmentation

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-26 DOI: 10.1016/j.measurement.2025.117136

Emre Guclu, ilhan Aydin, Erhan Akin

Ensuring high-quality production in the steel manufacturing industry is crucial for efficiency, waste reduction, and cost minimization. Traditional manual inspection methods are often inconsistent, time-consuming, and prone to human error, making automated visual inspection essential for reliable quality control. Steel surface defect detection plays a critical role in identifying issues such as cracks, scratches, and corrosion, which can compromise product durability and performance. This study proposes a new deep learning-based defect segmentation model to enhance the accuracy and efficiency of steel defect detection. The model incorporates ResNet50, Residual Block (RB), Residual Squeeze-and-Excitation Block (RSB), and Residual Refinement Module (RRM) to improve deep feature extraction and segmentation precision. Extensive evaluations demonstrate that the proposed model achieves an impressive 87.8% mean Intersection over Union (mIoU), outperforming existing segmentation models. A custom dataset was created using a real production line image acquisition system, ensuring diverse defect representation. Additionally, Synthetic Defect Generation (SDG) techniques were applied to enhance the dataset and improve model robustness. The proposed model offers a scalable and automated defect detection solution, significantly improving quality control, reducing inspection time, and ensuring higher reliability in industrial applications.

确保钢铁制造业的高质量生产对于提高效率、减少浪费和降低成本至关重要。传统的人工检测方法往往不连贯、耗时且容易出现人为错误，因此自动化视觉检测对于可靠的质量控制至关重要。钢材表面缺陷检测在识别裂纹、划痕和腐蚀等问题方面起着至关重要的作用，这些问题可能会影响产品的耐用性和性能。本研究提出了一种新的基于深度学习的缺陷分割模型，以提高钢铁缺陷检测的准确性和效率。该模型结合了 ResNet50、残差块（RB）、残差挤压激发块（RSB）和残差细化模块（RRM），以提高深度特征提取和分割精度。广泛的评估表明，所提出的模型达到了令人印象深刻的 87.8% 的平均联合交叉率（mIoU），优于现有的分割模型。利用真实的生产线图像采集系统创建了一个自定义数据集，确保了多样化的缺陷表示。此外，还应用了合成缺陷生成（SDG）技术来增强数据集并提高模型的鲁棒性。所提出的模型提供了一种可扩展的自动缺陷检测解决方案，大大改善了质量控制，缩短了检测时间，并确保了工业应用中更高的可靠性。

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引用次数: 0

A robust control scheme for optimized pitch angle estimation of offshore wind turbine under varied climatic conditions using Osprey algorithm

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-26 DOI: 10.1016/j.measurement.2025.117122

Prince Kumar, Nabanita Adhikary

This study presents a data-driven framework for optimizing power generation control in hybrid power networks, with a particular focus on enhancing performance and mitigating frequency fluctuations in systems integrated with offshore wind energy. The increasing complexity of modern power grids, driven by the growing penetration of renewable energy sources, presents significant challenges in maintaining grid stability. Offshore wind farms, as key contributors to sustainable energy, are central to this research, which evaluates their operational efficiency within a multi-area network under varying offshore climatic conditions. At the heart of this approach is an advanced control strategy that combines precise pitch angle estimation with a fractional-order controller, optimized using the Osprey algorithm. The proposed methodology dynamically adjusts the pitch angle of wind turbine blades to maintain an optimal tip-speed ratio, maximizing power generation while minimizing blade stall and drag effects. This control mechanism enhances generation stability and facilitates the seamless integration of offshore wind energy into hybrid power grids. Utilizing pitch angle estimated control strategy, system performance got improved from 260.06 to 37.51. The results underscore the effectiveness of the proposed strategy in improving the overall efficiency, reliability, and resilience of offshore wind-enriched networks, offering scalable solutions to address the integration challenges of offshore wind farms. By optimizing power generation and grid stability, this study contributes to the development of more sustainable and robust energy infrastructures, supporting the global transition to a greener energy future.

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引用次数: 0

Enhancing the high-frequency signal performance through surface morphological modification of Cu interconnects

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-26 DOI: 10.1016/j.measurement.2025.117071

Ying-Chih Chiang, Yu-Hsun Chang, Zhao-Yu Yang, Chun-Jou Yu, Wei-Ling Chou, Cheng-En Ho

Recently, the mobile communication community has expanded its operating frequency bands to the millimeter wave (mmWave) range to increase the transmission bandwidth, to meet the requirements of higher data transfer rates, lower latency, and greater data transmission capacity for wireless communications. In mmWave transmission, the skin effect might cause the majority of signals to be delivered near the conductor periphery, inducing noticeable conductor loss (signal degradation) due to signal scattering/reflections and surface inductance as a result of a rough conductor (Cu) surface. This study was conducted to modify the surface morphology of Cu interconnects through the Cu clad laminates (CCL) process utilizing different Cu foils, including high-temperature-elongation (HTE) foil, two types of reverse-treated foils (RTF), and hyper-very-low-profile (HVLP) foil, to promote the signal transmission performance of differential striplines in mmWave frequency bands. The signal loss (1–43.5 GHz) on differential striplines with different Cu interconnect roughness was characterized using the Groisse and Huray models through the use of a high-frequency structure simulator (HFSS). Furthermore, experimental measurements utilizing a vector network analyzer (VNA) were conducted to evaluate the signal loss resulting from various Cu foils, with the aim of validation the numerical simulation results. The HFSS simulation and VNA measurement results revealed that the Huray model enables to the characterization of high-frequency transmission behavior more accurately in rough Cu foils than the Groisse model does. Moreover, the HVLP-type Cu foils exhibited better high-frequency characteristics than the other Cu foils examined because of lower signal scattering/reflections and surface inductance as a consequence of their low-profile surface morphology. A detailed comparison between the VNA measurements and simulation models (Groisse and Huray) was made and the dependence of transmission loss on the Cu interconnect roughness was quantitatively analyzed in this study. These new data will not only advance our own fundamental knowledge of the high-frequency materials but also be highly beneficial for the development of mmWave transmission technologies.

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引用次数: 0