摘要
以离港航空器为研究对象,将废气排放因素纳入滑行路径优化模型,分别建立最短滑行时间模型、最小废气排放量模型和两者相结合的多目标优化模型,并采用遗传算法开展算例仿真以获取帕累托最优配置方案。同时,考虑到CO2在废气中占比最大,就CO2单位成本、CO2排放成本、废气排放成本和滑行总成本之间的关系进行敏感性分析。结果表明:相较于最短滑行时间模型,该多目标优化模型可在保证航空器滑行效率的同时减少约1%的废气;随着CO2单位成本的增加,CO2排放成本占废气排放成本的比重呈“先快后慢”的趋势,在CO2单位成本值较低的情况下,表现出高度敏感性,而在成本值较高的情况下,敏感性较不显著;废气排放成本占总成本比重近乎成等比例增长。
The shortest taxiing time model,the lowest carbon emission model and the multi-objective model for departure aircrafts are established considering pollutant emissions.Genetic algorithm is employed to conduct case simulation and generate Pareto optimal allocation solution.In addition,considering that CO2 emission accounts for the largest part of pollutant emissions,relation between CO2 unit cost,CO2 emission cost,pollutant emission cost and total cost is studied basing on sensitivity analysis.Experimental results show that:comparing with the shortest taxiing time model,the multi-objective model can keep aircraft taxiing efficiency while reducing 1%pollutant emissions;with CO2 unit cost increasing,the percentage of CO2 emission cost of pollutant emission cost increases rapidly which indicates high sensitivity when CO2 unit cost is low,and then slows down which shows non-significant sensitivity when CO2 unit cost is high,and the pollutant emission cost of total cost reflects almost positive proportional increase.
作者
郑丽君
胡荣
张军峰
朱佳琳
ZHENG Lijun;HU Rong;ZHANG Junfeng;ZHU Jialin(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《中国民航大学学报》
CAS
2019年第6期34-38,45,共6页
Journal of Civil Aviation University of China
基金
国家自然科学基金项目(71201082,71401072)
南京航空航天大学研究生创新基地(实验室)开放基金项目(kfjj20180706)
关键词
航空运输
滑行路径优化
遗传算法
离港航空器
废气排放
air transportation
taxiing route optimization
genetic algorithm
departure aircrafts
pollutant emission
