Sci-Tech

New LiDAR: Eliminating "Invisible Killers" in Flight

2024-05-08   

Wind shear refers to a sudden and drastic change in wind direction and speed, known as the "killer of flight safety". The international aviation community recognizes that low altitude wind shear is an important risk factor during the takeoff and landing stages of aircraft. Recently, the Xia Haiyun research group at the School of Atmospheric Physics, Nanjing University of Information Technology has successfully developed a 30 kilometer all-weather non field of view multifunctional LiDAR, which can accurately detect special atmospheric phenomena such as wind shear. Its maximum horizontal detection radius and vertical detection altitude reach 30 kilometers and 7 kilometers respectively, which is a global leading level. This radar can achieve all-weather remote sensing detection of multiple atmospheric parameters, and through the application of nanomaterials and artificial intelligence technology, it can also achieve accurate detection in harsh weather, building obstruction, and other situations. The relevant research results have been published in international journals such as Remote Sensing and Optical Express. Nanomaterials enhance the "vision" of LiDAR. Wind shear and clear air turbulence are special atmospheric phenomena, and they have a nickname that makes pilots from various countries fear - "invisible killers". The existence of these special atmospheric phenomena seriously affects the flight safety of civil aviation aircraft. The emergence of LiDAR provides the best solution at the current technological level to solve these problems. Xia Haiyun introduced that LiDAR has been developed for over half a century. The basic principle is that the emitted laser pulse interacts with the atmosphere, and an optical antenna is used to collect atmospheric backscatter signals, which are then input into an optical receiver. After photoelectric detection and data processing, a series of key atmospheric parameters are obtained. At present, LiDAR has been widely used in various detection tasks, such as aerosol concentration, PM2.5 value, cloud height, temperature, humidity, visibility, atmospheric components (such as water vapor, various pollutant gas components), etc. However, due to limited detection capabilities, traditional wind lidars still struggle to adapt to wind shear detection under various complex and adverse weather conditions. The research on wind lidar has therefore been listed by the World Meteorological Organization as the most challenging lidar research. "Usually, when encountering low visibility conditions such as rainfall and heavy fog, the performance of LiDAR is limited and cannot meet work requirements. Wind measurement LiDAR is often used in aviation meteorology, traffic meteorology, environmental monitoring, emergency management and other scenarios. Once the detection accuracy drops, it will greatly affect industry safety," Xia Haiyun explained. Xia Haiyun said that during rainfall, for example, the glass mirror of the LiDAR will be stained with rainwater. If it's drizzling rain, LiDAR is like a person wearing glasses into the bathroom, with foggy glasses and blurred vision; If the rainfall is heavy, the mirror of the LiDAR will become uneven, affecting the collimation effect. The Xia Haiyun research group developed a nanostructure through Jiangsu Feiwotai Nanotechnology Co., Ltd. and applied a layer of hydrophobic and oleophobic material on the glass mirror surface, making it difficult for oil stains and water droplets to accumulate on the lens, and allowing the laser to be emitted smoothly. "This layer of nanomaterial can withstand 600 wipes and can be used for 10 years based on wiping once a week," said Xia Haiyun. Kunming Changshui Airport in Yunnan Province is affected by warm and humid Indian Ocean currents, making it the airport with the highest probability of wind shear occurrence in southwestern China. In December 2021, the LiDAR developed by Xia Haiyun's research group was put into trial operation at Changshui Airport. Observational statistics for 9 months

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