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

作　　者：Xingyi Zhu Yanan Wu Yang Yang Yafeng Pang Hongwei Ling Dawei Zhang 

机构地区：[1]Key Laboratory of Road and Traffic Engineering of Ministry of Education,Tongji University,Shanghai 201804,China [2]BYD Auto Industry Company Limited,Hexagon Building,No.3009 of BYD Road,Pingshan,Shenzhen 518118,China [3]Shanghai Pudong Architectural Design&Research Institute Co.,Ltd,Shanghai 204201,China

出　　处：《International Journal of Transportation Science and Technology》2024年第1期77-90,共14页交通科学与技术(英文)

基　　金：The work described in this paper is supported by the National Natural Science Foundation of China(No.52278455);the Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(21SG24);the International Cooperation Project of Science and Technology Commission of Shanghai Municipality(No.22210710700);the Fundamental Research Funds for the Central Universities.

摘　　要：The safety of aircraft landing on wet runways is of great importance in runway risk man agement.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 f ilm-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 proto type 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 dis tance 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 run way,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.

关 键 词：Finite element method Virtual prototype Machine learning Real-time risk assessment CO-SIMULATION 

分 类 号：O17[理学—数学]