Prof. Li Deng Profile

Prof. Li Deng

at Civil Aviation Flight Univ of China

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Proceedings Article | 6 November 2023 Paper

A review on aircraft cargo compartment fire detection using dual-wavelength sensors

Li Deng, Dandan Wu, Jin Zhou, Yuanhua He, Quanyi Liu, Haibin Wang

Proceedings Volume 12921, 1292126 (2023) https://doi.org/10.1117/12.2689172

KEYWORDS: Fire, Atmospheric particles, Light scattering, Aerosols, Particles, Atmospheric modeling, Data modeling, Combustion, Safety, Infrared radiation

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Fire detection is one of the most important components of the aeroplane fire prevention system, particularly for not accessible cargo compartments (Class C, D, E). Although technology is rapidly developing, there are still defects and lags in technology updates at the application end, photoelectric smoke detectors determine fire state conditions based on the intensity of light scattered by smoke particles, while the light scattering effect of non-fire interference aerosols can also induce false alarm, leading to a certain extent to inefficient operation in terms of security. So far, the main development direction of fire detection is to identify the characteristics of fire smoke and interference sources according to the different aerosol particles present in the specific environment of aircraft cargo compartments to assure or even improve the early identification rate of fire, while minimizing false alarm rate of fire detection system. It is observed that C919 was the first to adopt dual-wavelength smoke detection technology during test flight. Compared with conventional photoelectric smoke detector, it applies infrared and blue wavelengths to further enhance the ability to identify smoke particles and resist false alarms by comparing the infrared and blue scattered light response signals of smoke particles and interference particles.

Proceedings Article | 24 June 2021 Paper

Experimental study on the burning images of oil pool fire in confined space

Li Deng, Lin Hu, Bo Zhu, Quanyi Liu, Yuanhua He, Haibin Wang, Qin Wang

Proceedings Volume 11885, 118850T (2021) https://doi.org/10.1117/12.2602337

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Due to the lack of visual information in conventional fire detection methods, false alarms and missing alarms are easy to occur. N-heptane and aviation kerosene were used as experimental fuels to carry out oil pool fire combustion experiment. Combining the convolutional neural network and the principle of image correlation to analyze the classification and recognition of combustion images and the frequency of flame oscillation, which were used as parameters to determine whether a fire occurred. The results show that the peak flame temperature and increasing rate of temperature of combustion is slightly lower by low pressure, which cause jitter of temperature and the trend is more obvious. The O2 decreases firstly and then increases and finally tends to be stable, while, the content of CO2 increases firstly and then decreases and finally comes to be stable at 96kPa. However, Under the equidistant combustion time series of n-heptane and aviation kerosene, the average flame oscillation frequency is 3.26 Hz and 3.06 Hz, respectively, and the error between its theoretical flame oscillation frequency is small, and the accuracy rate is as high as 90%. It can be seen that low pressure has a greater influence on the fire behavior of combustion, which could provide theoretical support for studying the key technologies of fire alarm under low pressure.

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