Study On The Optimization Algorithm Of Flight Plan Software For Domestic Civil Aircraft

With the development of domestic civil aircraft,related aircraft performance software is also developed and optimized.As an important part of aircraft performance software,flight plan plays an important role in ensuring the safety of civil aviation operation and improving the operating economy of airlines,Since the flight plan calculation involves frequent calls and repeated iterations of aircraft high-speed performance modules,and the calculation time is long,it becomes necessary to study the flight plan optimization algorithm to improve the calculation efficiency.Based on the review of aircraft performance and the current status of domestic and international research on flight planning software and intelligent algorithms,the traditional manual and computer flight plan formulation process based on CCAR-121-R7 new fuel policy was being improved,and a method to optimize the traditional computer flight plan algorithm combining with neural network was being proposed.A BP neural network optimized by cuckoo search algorithm was being established to improve the flight planning algorithm,and the neural network structure was being determined by the analysis of arithmetic cases.Combined with the parameter sensitivity analysis method,the influence degree of the flight plan input parameters on the target parameters was being analyzed to provide a reference for extracting training sample data.A validation program was written by using MATLAB and FORTRAN software.The calculation results show that the flight plan optimization algorithm based on the improved BP neural network can maintain the calculation accuracy and significantly improve the calculation efficiency,which provides a feasible solution for the development and optimization of domestic civil aircraft performance software.