A beam-membrane structure micromachined differential pressure flow sensor

2015 ◽  
Vol 86 (4) ◽  
pp. 045004 ◽  
Author(s):  
P. Chen ◽  
Y. L. Zhao ◽  
B. Tian ◽  
C. Li ◽  
Y. Y. Li
Keyword(s):  
Membrane Structure ◽  
Differential Pressure ◽  
Flow Sensor ◽  
Pressure Flow

Experimental Investigation of Static Pressure Characteristics of FBG Differential Pressure Flow Sensor

2013 ◽  
Vol 336-338 ◽  
pp. 134-138 ◽  
Author(s):  
Chun Tong Liu ◽  
Zhen Xin He ◽  
Yang Zhang ◽  
Hong Cai Li
Keyword(s):  
Static Pressure ◽  
Sensitivity Coefficient ◽  
Differential Pressure ◽  
Flow Sensor ◽  
Experimental Result ◽  
Hydraulic Pressure ◽  
Hysteresis Phenomenon ◽  
Pressure Flow ◽  
Pressure Calibrator ◽  
Pressure Characteristics

On the basis of FBG (Fiber Bragg Grating) sensor principle analysis, a differential pressure flow sensor using FBG has been designed. The static pressure characteristics of the sensor were experimental studied by the hydraulic pressure calibrator and Q8384 spectrometer, and the experimental result errors were analyzed. Experimental results show that, the sensitivity coefficient of FBG is 3 pm/KPa in the differential pressure range of 0~0.35 MPa. The changes of Bragg wavelength with the pressure changes showing a good linear relationship and repetitive, and the hysteresis phenomenon is minor, which can be used for flow measurement of hydraulic system in special areas.

scholarly journals Error Compensation Technique for a Resistance-Type Differential Pressure Flow Sensor

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Guimei Wang ◽  
Tao Chu ◽  
Lijie Yang ◽  
Fang Sun
Keyword(s):  
Least Squares ◽  
Error Compensation ◽  
Differential Pressure ◽  
Flow Sensor ◽  
Support Vector ◽  
Model Parameters ◽  
Filling Material ◽  
Pressure Flow ◽  
Compensation Technique ◽  
Fourier Series Approximation

A flow sensor is designed based on resistance-type differential pressure flow (RDPF) method, and the flow data is measured during a coal gangue paste-filling process. The measurement error characteristics of a RDPF sensor are analyzed. Periodic and aperiodic errors are then modeled separately. The model for the periodic error is established by Fourier series approximation using least squares solution of an overdetermined equation to solve for the model parameters. The model for the aperiodic error is established using an online least squares support vector machine (LS-SVM) method. The cross-validation is used to solve model parameters. Simulations and experiments show that the dynamic measurement accuracy of the sensor is greatly improved by error compensation, thereby reducing filling material waste and improving the economic efficiency.

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