Real-time risk assessment of aircraft landing based on finite element-virtual prototype-machine learning co-simulation on wet runways

Abstract

The safety of aircraft landing on wet runways is of great importance in runway risk management. In order to ensure landing safety on wet runways, real-time risk warning is required. This paper proposes a method to assess aircraft landing risk in real-time based on finite element-virtual prototype-machine learning co-simulation. Firstly, a tire-water film-runway finite element model was constructed, a virtual prototype model was built based on the Airbus A320 model, and the results of the tire-water film-runway local finite element dynamic analysis were transferred to the system simulation of the virtual prototype for co-simulation. Secondly, considering the influence of wet state parameters on the runway, a database of aircraft anti-skid failure risk was constructed, and three machine learning models were trained to predict aircraft landing risk. The results show that the Support Vector Machine (SVM) model has better generalization capability and should be used to predict the risk level of aircraft landing. The efficacy of the comprehensive taxiing model was validated using an empirical formula for determining the aircraft's landing distance on a wet runway. When an aircraft lands on a runway with an average water film thickness of 8 mm, the braking time is approximately 1.6 times longer than on a dry runway, and the braking distance is roughly 5.3 times greater than on a dry runway. Finally, a risk assessment example was provided: the entire process from landing information input to risk level output for the aircraft model took only 80 ms, which could provide an efficient and real-time aircraft landing risk assessment.