
Research on Pressure Loss and Compensation in Automatic Detection of Pressure Quality of Hydrocephalus Shunt
Author(s) -
Quancheng Dong,
Ping Ji,
Min Wan,
Kai Shi,
Xuan Sun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/4/042091
Subject(s) - shunt (medical) , mechanics , pressure drop , pipeline (software) , pressure sensor , hydrocephalus , transmission loss , compensation (psychology) , acoustics , materials science , simulation , engineering , mechanical engineering , physics , medicine , psychology , psychoanalysis , cardiology , radiology
In the automatic pressure quality detection of hydrocephalus shunts, the extension pipes installed on the test piece, the mounting base of the sensor, etc. will cause pressure loss, resulting in distortion of the detection results. This paper uses CFD software to simulate and analyze the pressure loss along the way caused by the extension pipeline and the local pressure loss caused by the installation base. According to the factors affecting the pressure loss along the way such as liquid flow rate, pipeline inner diameter, pipeline length, etc., the pressure loss along the way under different pipeline lengths, different pipeline inner diameters, and different flow speeds are measured, and single-factor and multi-factor influences are carried out according to the simulation data. Analyze and establish a regression model for pressure compensation. According to the main influencing factors of local pressure loss, sudden reduction ratio, and flow rate, pressure field analysis is carried out. The simulation results show that single factors such as flow velocity and inner diameter, as well as the coupling effect of pipeline inner diameter and flow velocity, have a significant impact on pressure loss along the way, and an increase in the sudden reduction ratio will lead to an increase in local pressure loss. Experimental verification shows that the constructed pressure loss regression model can effectively compensate for the automatic pressure detection of hydrocephalus shunt.