A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.     

液气式针栓喷注单元喷雾场结构试验研究

径向孔型针栓喷注器相对于径向缝型针栓喷注器具有更复杂的喷雾场。为了研究径向孔型针栓喷注器的喷雾场结构，将径向孔型针栓喷注器简化为单个气体射流与液膜碰撞的针栓喷注单元，采用了背景光成像系统结合激光相位多普勒技术（PDA），以水和空气为模拟介质，对液气式针栓喷注单元的喷雾场进行了试验研究。试验结果表明，液气式针栓喷注单元喷雾的三维结构呈现“喇叭”状。根据喷雾的形成过程及液滴的分布，液气式针栓喷注单元喷雾可以划分为4个区域：碰撞区、液滴区、液雾区及液丝区。液气式针栓喷注单元喷雾的分布范围可由内边界角、外边界角、中线角及散布角表示，均随局部动量比的增大而增大。液滴区的粒子主要由碰撞过程产生，SMD较大；液雾区的粒子经碰撞过程产生后，在气动力作用下进一步雾化，SMD小。由于液雾区的速度和粒径同时受到气动力作用的影响，粒径分布与速度分布在空间上呈现负相关趋势。