Non-contact flow rate detection of component in mixed gas using spectrum absorption theory

Han, Bo; Zhang, Yanan; Wang, Xu; Zhou, Fu-dong; Li, Tao; Guo, Hao; Chen, Shanshan; Yuan, Jinlong

doi:10.1016/j.yofte.2018.07.021

Cited by 2 publications

(1 citation statement)

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“…Tissue blood flow was evaluated using a laser Doppler flowmeter during vascular responses to physiological stimuli by measuring the index of the average flux of red cells as well as the pulsatile components [ 6 ]. A non-contact flow rate sensing method for mixed gas components was proposed based on an optical lens and photo-detector, and the flow rates of each gas component in the gas mixture were identified [ 7 ]. An ultrasonic flowmeter was used in natural gas metrology and process control areas based on the principle of the difference in transit time between upstream and downstream flows [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Flowmeter Using Vibration Interaction between Gauge Plate and External Flow Analyzed by LSTM

Jin

Chung

Park

2020

Sensors

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(1) Background: This study is aimed at the development of a precise and inexpensive device for flow information measurement for external flow. This novel flowmeter uses an LSTM (long short-term memory) neural network algorithm to analyze the vibration responses of the gauge plate. (2) Methods: A signal processing method using an LSTM neural network is proposed for the development of mass flow rate estimation by sensing the vibration responses of a gauge plate. An FFT (fast Fourier transform) and an STFT (short-time Fourier transform) were used to analyze the vibration characteristics of the gauge plate depending on the mass flow rate. For precise measurements, the vibration level and roughness were computed and used as input features. The actual mass flow rate measured by using a weight transducer was employed as the output features for the LSTM prediction model. (3) Results: The estimated flow rate matched the actual measured mass flow rate very closely. The deviations in measurements for the total mass flow were less than 6%. (4) Conclusions: The estimation of the mass flow rate for external flow through the proposed flowmeter by use of vibration responses analyzed by the LSTM neural network was proposed and verified.

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Section: Introductionmentioning

confidence: 99%