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

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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.

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... 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.
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
Study of the performance-based fire-protection design for large-space exhibition building
  • Q Yang