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Featured Application: This study is aimed at the detection of sulfur dioxide concentration in the real environment. Due to the vast territory of China and the large temperature difference span between the north and the south, the current sulfur dioxide detection devices find it difficult to meet the requirements of portability, low-cost delivery, and temperature adaptability. A reliable real-time monitoring device and detection algorithm for sulfur dioxide concentration using PMT are proposed. Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO2) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO2 is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO2 concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained. [ABSTRACT FROM AUTHOR]

Purpose: It is an indispensable part of airworthiness certification to evaluate the fuel tank flammability exposure time for transport aircraft. There are many factors and complex coupling relationships affecting the fuel tank flammability exposure time. The current work not only lacks a comprehensive analysis of these factors but also lacks the significance of each factor, the interaction relationship and the prediction method of flammability exposure time. The lack of research in these aspects seriously restricts the smooth development of the airworthiness forensics work of domestic large aircraft. This paper aims to clarify the internal relationship between user input parameters and predict the flammability exposure time of fuel tanks for transport aircraft. Design/methodology/approach: Based on the requirements of airworthiness certification for large aircraft, an in-depth analysis of the Monte Carlo flammability evaluation source procedures specified in China Civil Aviation Regulation/FAR25 airworthiness regulations was made, the internal relationship between factors affecting the fuel tank flammability exposure time was clarified and the significant effects and interactions of input parameters in the Monte Carlo evaluation model were studied using the response surface method. And the BP artificial neural network training samples with high significance factors were used to establish the prediction model of flammability exposure time. Findings: The input parameters in the Monte Carlo program directly or indirectly affect the fuel tank flammability exposure time by means of the influence on the flammability limit or fuel temperature. Among the factors affecting flammability exposure time, the cruising Mach number, balance temperature difference and maximum range are the most significant, and they are all positively correlated with flammability exposure time. Although there are interactions among all factors, the degree of influence on flammability exposure time is not the same. The interaction between maximum range and equilibrium temperature difference is more significant than other factors. The prediction model of flammability exposure time based on multifactor interaction and BP neural network has good accuracy and can be applied to the prediction of fuel tank flammability exposure time. Originality/value: The flammability exposure time prediction model was established based on multifactor interaction and BP neural network. The limited test results were combined with intelligent algorithm to achieve rapid prediction, which saved the test cost and time. [ABSTRACT FROM AUTHOR]