基于蒙特卡罗轨迹偏差的机场临时机位划设优化

doi:10.16265/j.cnki.issn1003-3033.2023.11.1021

摘要/Abstract

摘要：

为解决枢纽机场停机位的满负荷甚至超负荷运行的安全和效率问题,利用运动学模型和蒙特卡罗轨迹偏差方法,研究临时机位的划设。分析B构型和停靠廊桥对临时机位设置的影响,建立航空器在机坪区域的运动方程;基于离散统计理论,建立航空器在正常机位的滑入推出和直线运行状态的蒙特卡罗轨迹偏差模型;利用Matlab仿真的标称轨迹和偏差区域,构建设有临时机位时的机位运行冲突概率模型;并以长沙黄花机场G滑行道区域30个正常机位4个冲突热点进行实例验证。结果表明:相比传统方案,基于蒙特卡罗的轨迹偏差能够提高运行冲突区域的精准判断,影响正常机位数量降低10%,推出路线干扰数量降低50%,冲突概率减少56.8%,消除3组的对头滑行,优化临时机位的划设,降低运行风险冲突。

关键词: 蒙特卡罗, 轨迹偏差, 临时机位划设, 轨迹模拟, 冲突概率

Abstract:

In order to solve the safety and efficiency of hub airport aprons during full load or even overload operation of apron stands, the Kinematics model and Monte Carlo trajectory deviation method were used to study the allocation of temporary stands for aircraft. The kinematics model of aircraft taxing on an apron area was established by analyzing the influence of B configuration and docking corridor bridge. The Monte Carlo trajectory deviation model of aircraft in a fixed stand was put forward based on discrete statistical theory. Combined with the nominal trajectory and deviation area of Matlab simulation, the apron operation conflict probability model was constructed with temporary stands. The case verification of 30 normal stands and 4 hot spots on taxiway G of Changsha Huanghua Airport was carried out. The results show that compared with the traditional manual experience scheme, the Monte Carlo based fluorescence detection method can improve the accuracy judgment of operation conflict area, reduce the amount of affected fixed stands by 10%, interference route for push-out by 50% and conflict probability by 56.8%. Three groups of head-to-head gliding are eliminated. The research is helpful in optimizing the allocation of temporary stands and reducing risk of operation conflict.

Key words: Monte Carlo, trajectory deviation, temporary stand setting, trajectory simulation, conflict probability

陈华群, 朱兴澳, 于海旺. 基于蒙特卡罗轨迹偏差的机场临时机位划设优化[J]. 中国安全科学学报, 2023, 33(11): 82-88.

CHEN Huaqun, ZHU Xing'ao, YU Haiwang. Allocation optimization of temporary parking in airport based on Monte Carlo trajectory deviation[J]. China Safety Science Journal, 2023, 33(11): 82-88.

图/表 8

图1

图2

表1

机位和航空器参数取值

项目		参数值	项目	参数值
相邻机位净距		7.5	轮间距	A320	16.91
车道边线净距		3	轮间距	A330	25.37
机身长度Z_i	A320	44.51	$e i j$	A320	4.5
机身长度Z_i	A330	63.69	$e i j$	A330	7.5
翼展	A320	34.1	e^j^,^j	76
翼展	A330	60.3	L^j^,^j⁺¹	50
前后轴距	A320	16.78	H	10
前后轴距	A330	32.04	ΔD_i	3
每秒直线滑行距离		13	N	9
每秒转弯滑行距离		5	M	5

表1

图3

图4

图5

图6

图7

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