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Analysis of Water Jet Trajectory of Auto-Targeting Fire Sprinkler System in Interior Large Space

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Advanced Materials Research
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Abstract

Auto-targeting fire sprinkler system is an intelligent fire extinguishing equipment that used in interior large space. An auto-targeting fire sprinkler system was designed; the compositions and working principle were also expounded in detail. The water trajectory equation was deduced and simulated by Matlab software according to the principle of particle kinematics, ballistics, and fluid mechanics. The relationship among working pressure, pitch angle, installation height and jet range, flow landing speed was analyzed. The results show that the fire sprinkler system can satisfy the design requirements, and the water trajectory equation basically concides with the actual situation, which can provide some theoretical references for the fire precision location.
Analysis of Water Jet Trajectory of Auto-targeting Fire Sprinkler System
in Interior Large Space
Ming Long
a
, Guoliang Hu
b
and Zhong Li
c
School of Mechanical & Electronical Engineering, East China Jiaotong University, Nanchang,
Jiangxi 330013, P.R. China
aminglong1963@163.com, bglhu2006@163.com, clizhong19850707@163.com
Keywords: Interior large space, Fire sprinkler system, Auto-targeting, Water trajectory.
Abstract. Auto-targeting fire sprinkler system is an intelligent fire extinguishing equipment that used
in interior large space. An auto-targeting fire sprinkler system was designed; the compositions and
working principle were also expounded in detail. The water trajectory equation was deduced and
simulated by Matlab software according to the principle of particle kinematics, ballistics, and fluid
mechanics. The relationship among working pressure, pitch angle, installation height and jet range,
flow landing speed was analyzed. The results show that the fire sprinkler system can satisfy the
design requirements, and the water trajectory equation basically concides with the actual situation,
which can provide some theoretical references for the fire precision location.
Introduction
With the rapid development of interior large space building, the fire prevention of interior large space
building has been more and more important. On the other hand, the traditional fire extinguishing
equipments can not satisfy the fire preventing requirements because of the characteristics of big
dimensions, high height and densely population in the interior large space [1-2]. So, it is necessary to
carry out auto-targeting research of fire sprinkler system in interior large space.
At present, the researches at home and abroad are only focused on the fire location when the fire
happened [3-4], and there is little attention on the water trajectory, which can lead to poor precision of
fire location, and much water is also needed to put out the fire. In this paper, a auto-targeting fire
sprinkler system of interior large space was designed, modeling and simulations of water trajectory
are also carried out to obtain the relationships among the working pressure, pitch angle, installation
height and jet range, which can provide some guidance for the fire location.
Structural design of the auto-targeting fire sprinkler system
The structure of the fire sprinkler system should be compactness and simplification in the design, so,
the drive forms with large space are unnecessary. In addition, the precisions of fire location should
also be high enough. Considering the advantages of smooth working, high efficiency and long service
life in the gear drive and the compact structures, anti-stroke and self-locking abilities of the worm
drive, the composite transmissions that including gear drive and worm drive are used as the basic
power transmission in the fire sprinkler system.
The structural diagram of the auto-targeting fire sprinkler system is shown in Fig. 1. It is mainly
composed by horizontal transmission mechanism, pitching transmission mechanism and relevant
detecting parts. The water is pressed into the system through external water supply system that
connected with flange by pipe threads. The whole system was installed on the roof of the interior
large space, and the starting detector always monitors the whole large space. When the fire in the
interior large space was detected, the fire signal would be transmitted to the host controller, and the
host controller would alarm. At the same time, the stepper motor will drive the horizontal worm gear
to rotate. The horizontal small gear and the horizontal big gear will rotate too. So, the system can
rotate in the horizontal direction. The level detector scans the fire signal and calculates the midpoint
value between the signals first received and disappears as the centre of fire in level. Meanwhile,
Advanced Materials Research Online: 2012-03-15
ISSN: 1662-8985, Vols. 490-495, pp 171-175
doi:10.4028/www.scientific.net/AMR.490-495.171
© 2012 Trans Tech Publications Ltd, All Rights Reserved
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans
Tech Publications Ltd, www.scientific.net. (Research Gate for subscription journals-03/03/25,09:37:48)
... Through the analysis of forces in the air, the water jet trajectory equation, under the action of many influential factors, was established, and a simulation analysis of the jet trajectory of a fire gun was carried out by using MATLAB software, this method [14] overcame the problem proposed by Wan feng [12] by not constraining the working pressure and the flow parameter. Hu Guoliang and others [15] pointed out that due to a series of external factors, even if the water is injected into the space at the centre of the fire, the final flow point may exceed the scope of the margin of error for effective extinguishing. Therefore, considering gravity and the air resistance of a water flow, a force analysis was carried out. ...
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Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored external factors, such as environmental conditions and the status of the fire engine, even though the water jet falling point location prediction was often associated with these parameters and showed a nonlinear relationship. This paper constructed a BP (Back Propagation) neural network model. The fire gun nozzle characteristics were included as model inputs, and the water discharge point coordinates were the model outputs; thus, the model could precisely predict the water discharge point with small error and high precision to determine an accurate firing position and allow for the timely adjustment of the spray gun. To improve the slow convergence and local optimality problems of the BP neural network (BPNN), this paper further used a genetic algorithm to optimize the BPNN (GA-BPNN). The BPNN can be used to optimize the weights in the network to train them for global optimization. A genetic algorithm was introduced into the neural network approach, and the water jet landing prediction model was further improved. The simulation results showed that the prediction accuracy of the GA-BP model was better than that of the BPNN alone. The established model can accurately predict the location of the water jet, making the prediction results more useful for firefighters.
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In large space structures, the latest fire detection methods are based on video image processing and data fusion. But the false positive rate and false negative rate remain unsatisfactory and need improving. The emphases of this paper are target extraction and recognition. A new adaptively updating target extraction algorithm (NAUTEA) is proposed by which the intact target can be extracted in time. In addition, some fire video image recognition algorithms, such as fuzzy neural network (FNN) and FGALSSVM (Fuzzy GALSSVM), are studied and improved. To verify the performance of these algorithms, a prototype system is developed, and a series of algorithm tests on a fire video are conducted. These tests make it clear that, the accurate, robust and real-time fire detection can be realized. KeywordsNAUTEA–Probability density algorithm–FNN–FGALSSVM–Dempster–Shafer (DS)–Historical data fusion
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