Optimization of electromagnetic flow measurement system based on a new mesh electrode

Ge, Liang; Wang, Sijin; Li, Ling; Chen, Junxian; Ji, Zhongli; Liu, Zhen; Wei, Guohui; Xiao, Xiaoting

doi:10.1016/j.measurement.2023.113012

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…The flow-sensing mechanism can be categorized into nonthermal and thermal types, depending on the presence/absence of heating elements. Nonthermal flow sensors utilize differential pressure, [1,2] electromagnetic, [3,4] ultrasonic, [5,6] and Coriolis effects [7,8] from fluid flow. Additionally, moving structures such as cantilevers [9,10] and turbines [11,12] can be used to measure the flow rate.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Shin,

Baek,

Choi

et al. 2024

Advanced Intelligent Systems

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This article presents a novel airflow rate sensing method based on the principle of a hot‐film flow sensing device with data‐driven machine learning (ML) models. In addition, to combine the two signals of the sensor (the resistance changes of the heaters) and predict the output (airflow rate), three different ML multivariate regression models, i.e., multiple linear regression (MLR), k‐nearest neighbor (KNN), and deep neural network (DNN) models, are trained and compared using 8400 experimentally obtained data. Using sensor fusion techniques, the average mean absolute error (MAE) and mean squared error (MSE) of the KNN model are determined to be 0.01522 and 0.00132, respectively, in the range of 0–5.07 standard liters per minute. Compared with the average results obtained using only a single input, those obtained using a dual input indicate a significant decrease in the MAE and MSE by 85.69% and 96.68%, respectively. Furthermore, a transient analysis of the ML‐based flow sensor is conducted to investigate the response time and transient characteristics of the MLR, KNN, and DNN models. The results of this study contribute to the advancement of airflow management systems for various industrial applications, such as building ventilation, gas leakage detection, and energy systems.

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Section: Introductionmentioning

confidence: 99%

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Shin,

Baek,

Choi

et al. 2024

Advanced Intelligent Systems

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show abstract

“…Shi et al [7] introduced and optimized an electromagnetic flowmeter with a double circular arc electrode for twophase flow measurement, validating its efficacy via simulation experiments. Ge et al [8] proposed a new type of mesh electrode for the shortcomings of existing electrode optimization, which effectively improves the accuracy of flow measurement in low flow rates and strong interference environments. Zou et al [9] started from the perspective of hydrodynamics to develop the optimal design of the electrode and the shape of the shrinkage cross-section, and the results of the study showed that the conical electrode can reduce the velocity fluctuation and the Widosinski curve shrinkage cross-section model has the best velocity flow line.…”

Section: Introductionmentioning

confidence: 99%

Research on optimization technology of excitation coil in downhole annular flow electromagnetic measurement system

Ge,

Zhou,

Xiao

et al. 2023

Meas. Sci. Technol.

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External interference and non-uniform velocity distribution influence the downhole annular flow electromagnetic measurement, reducing detection accuracy. Optimizing the structure of the excitation coil can effectively enhance the uniformity of magnetic field distribution, constituting an essential approach for improving measurement accuracy. Aiming at the excitation coil optimization technology of annular flow electromagnetic measurement system, the study first analyzes and obtains the magnetic field model of the excitation coil based on the principle of annular flow electromagnetic measurement, combined with the theory of vector weight function and puts forward the optimization theory of annular excitation coil. Secondly, the simulation models of rectangular and saddle exciting coils are established using finite element software to analyze the magnetic field distribution of annular flow under different coil and core sizes. The optimal structural parameters of the exciting coils in the annular flow electromagnetic measurement system are determined through evaluation indexes for the magnetic field. Finally, an experimental platform was established for optimizing the electromagnetic measurement of annular flow, and this platform was used to conduct comparative experiments. The experimental results show that the maximum reduction of the optimized system is 3.192%, 1.289%, and 1.813% for the single verification relative value error, single-point verification relative value error, and repeatability compared with the rectangular excitation coil annular flow electromagnetic measurement system. In the flow measurement interval of 25–100 m3 h−1, the single-point verification of the optimized system has a maximum relative display value error of 0.378%, and the measurement accuracy reaches 1.0 level and meets the consistency of the transmission of quantity and value. The research findings hold significant guidance for enhancing the measurement accuracy of the downhole annular flow electromagnetic measurement system.

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Electromagnetic Driving for Liquid Metal

Li,

Deng

2024

Handbook of Liquid Metals

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Optimization of electromagnetic flow measurement system based on a new mesh electrode

Cited by 6 publications

References 29 publications

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Research on optimization technology of excitation coil in downhole annular flow electromagnetic measurement system

Electromagnetic Driving for Liquid Metal

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