Abstract

The ventilation work is an important step to be completed before the start of the positive pressure explosion-proof robot. The existing explosion-proof technology uses constant pressure inflation, which will cause explosive gas to accumulate in the corner area of the cavity for a long time. In order to solve this problem, a ventilation method with periodic pulse intake is proposed. Based on the finite element method, the cleaning and ventilation process of the positive pressure explosion-proof robot is simulated and analyzed. The concentration of explosive gas in the robot cavity with time under constant pressure intake and pulse intake with different periods and amplitudes is compared. The simulation results show that the pulse intake is beneficial to the ventilation of the corner position. The period and amplitude of the pulse intake has an effect on the ventilation efficiency, when the period is the same, the greater the amplitude of the pulse intake, the higher the ventilation efficiency; when the amplitude is the same, the smaller the period of the pulse intake, the higher the ventilation efficiency. After experimental verification, the validity of the simulation results is proved. This study helps to improve the ventilation efficiency of positive-pressure explosion-proof robots and provides guidance for practical applications.

Details

Title
Influence of periodic pulse intake on the ventilation efficiency of positive pressure explosion-proof robot
Author
Fang, Ming 1 ; Chu, Xufeng 2 ; Yu, Liang 2 ; Fang, Yu 3 ; Hou, Liangliang 4 ; Cheng, Xu 2 ; Wang, Junlong 2 

 Anhui Polytechnic University, School of Artificial Intelligence, Wuhu, China (GRID:grid.461986.4) (ISNI:0000 0004 1760 7968); Efort Intelligent Equipment Co., Ltd, R&D Center, Wuhu, China (GRID:grid.461986.4) 
 Anhui Polytechnic University, School of Mechanical Engineering, Wuhu, China (GRID:grid.461986.4) (ISNI:0000 0004 1760 7968) 
 Efort Intelligent Equipment Co., Ltd, R&D Center, Wuhu, China (GRID:grid.461986.4) 
 Anhui Polytechnic University, School of Artificial Intelligence, Wuhu, China (GRID:grid.461986.4) (ISNI:0000 0004 1760 7968) 
Pages
1433
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2915455365
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.