Flight Level Allocation Optimization to Reduce Greenhouse Effect
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摘要: 为减小扇区空中交通飞行对环境的影响，使用高度层分配优化方法开展了基于降低温室效应的航空器绿色轨迹优化研究.首先针对扇区航空器运行的特点，结合区域扇区飞行冲突解脱方法，综合考虑扇区空中交通排放的CO2和产生的凝结尾对全球地表温度变化的影响；其次，以最小化全球地表温度变化为目标，建立区域扇区飞行调配模型，选用高度改变冲突调配策略，并采用带精英保留策略的遗传算法求解模型；最后，使用某市02号高空管制区的实际运行数据进行了实例验证.研究结果表明：该模型能够显著降低扇区航空器运行造成的全球地表温度增加；在25、50 a和100 a时间水平下全球地表温度增加值分别降低了98.74%、97.69%、97.11%；飞行高度层分配优化能大幅度降低温室效应.Abstract: To reduce the regional environmental impacts of air traffic flow, this study investigated a green trajectory optimization method for reducing greenhouse effect. According to the characteristics of the regional sector aircraft and the flight conflict resolution method, the influences of CO2 emissions and aircraft contrails on global surface temperature change were analysed and a regional aircraft deployment model was established to minimise the greenhouse effect. A genetic algorithm based on the elite retention strategy was designed. The feasibility of this algorithm was verified using real flight operations data of ZBAAAR02 as an example. The calculation result shows that the model can effectively reduce the rise in global temperature due to flight operations in the sector. The global surface temperature can decrease by 98.74%, 97.69%, and 97.11% in 25, 50, and 100 years respectively with this model. The results obtained suggests that flight level allocation model optimisation can effectively reduce the greenhouse effect.
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Key words:
- greenhouse effect /
- contrail /
- flight level allocation /
- genetic algorithm
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图 2 不同时间水平下种群进化过程中全球地表温度变化趋势
Figure 2. Trend of global surface temperature during population evolution process(different years)
图 3 50 a时间水平下高度调整策略下扇区冲突次数的变化
Figure 3. Conflict number under height change strategy at the level of 50 years
表 1 扇区高度层调配编码方法
Table 1. Coding mode of sector flight level allocation
序号 m1 m2 … mi … mu 编码 v1 v2 vi vu n1 n2 … ni … nu 
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表 2 各飞行高度层的凝结尾生成状况
Table 2. Contrail generation of flight level
高度/m RW/% RC/% RI/% 是否生成凝结尾 7 800 20.00 0 65.057 6 否 8 100 20.00 2.34 65.918 0 否 8 400 19.98 4.62 65.026 1 否 8 900 19.90 4.78 68.945 7 否 9 200 18.92 7.71 73.721 3 否 9 500 18.71 10.34 80.167 9 否 9 800 18.23 11.04 87.016 9 否 10 100 18.00 16.90 94.991 9 否 10 400 18.00 0 104.176 0 是 10 700 18.00 0 114.808 1 是 11 000 18.00 0 127.185 8 是 11 300 18.00 0 141.682 3 是 11 600 18.00 0 158.770 0 是 11 900 18.00 0 179.050 9 是 12 200 18.00 0 203.300 4 是 12 500 18.00 0 232.525 9 是
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表 3 全球地表温度变化前后对比
Table 3. Comparison of global surface temperature change
时间水平/a 优化前温度/℃ 优化后温度/℃ 优化前后全球地表温度改变率/% 25 2.66×10-9 3.36×10-11 98.74 50 1.24×10-9 2.87×10-11 97.69 100 1.05×10-9 3.03×10-11 97.11
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表 4 扇区航空器运行轨迹优化前后各温室效应影响因素增温对比
Table 4. Comparison of global surface temperature change before and after optimization of aircraft trajectory
时间水平/a 优化前全球地表温度增加值/℃ 优化后全球地表温度增加值/℃ 优化前后全球地表温度比值 CO2排放 凝结尾 CO2排放 凝结尾 CO2排放 凝结尾 25 1.00×10-9 1.66×10-9 2.53×10-11 8.29×10-12 39.59 200.39 50 8.57×10-10 3.87×10-10 2.63×10-11 2.35×10-12 32.56 164.82 100 7.63×10-10 2.82×10-10 2.82×10-11 2.06×10-12 27.07 136.99
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