Abstract

The encounter of vortices generated by a leading aircraft during takeoff or landing can be a source of hazard to a following aircraft. In spite of airport efforts to keep safe separation distances between aircrafts, a number of them encounter severe vortices each year. It has been challenging to accurately identify those encounters by manual approaches. To mitigate the impact of vortex encounters on an aircraft, it is important that more reliable identification techniques be developed. This research is a contribution towards the automatic identification of vortex encounters using artificial neural networks. Multilayer feedforward neural networks are trained using the back-propagation learning algorithm to classify flight events into either vortex encounters or other events. Using salient inputs such as aircraft roll angle, normal acceleration and lateral acceleration, the neural networks are able to achieve an overall average identification rate of about 88%. These results confirm the authors’ earlier assumption on using a reduced set of critical inputs to properly classify aircraft vortex encounters.