一个卷积神经网络预测模型,用于所有飞行阶段的航空氧化排放
Longfei Chen1, Qian Zhang2, Meiyin Zhu3
1State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; International Innovation Institute, Beihang University, Hangzhou 311115, China; School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
The Science of the total environment
|April 14, 2024
概括
一个新的模型使用卷积神经网络 (CNN) 准确预测航空氧化 (NOx) 排放. 这一进步通过为地面和巡航阶段创建全面的NOx排放清单,改善了气候和空气质量评估.
科学领域:
- 航空环境科学 航空环境科学
- 大气中的化学成分
- 气候建模 气候建模
背景情况:
- 航空业的氧化物 (NOx) 排放对大气组成产生重大影响,特别是对上层热带层的臭氧形成.
- 现有的氧化物排放数据库不完整,由于采样成本和不确定性,巡航阶段的数据很少.
- 覆盖地面和巡航阶段的全面排放数据库对于准确的航空环境影响评估至关重要.
研究的目的:
- 开发航空氧化 (NOx) 排放指数的新型预测模型.
- 准确预测各种发动机类型和燃料的地面和巡航阶段的NOx排放量,包括可持续航空燃料 (SAF).
- 建立一个更完整,更准确的航空NOx排放清单的基础.
主要方法:
- 利用卷积神经网络 (CNN) 技术创建了NOx排放指数的预测模型.
- 来自国际民航组织 (ICAO) 发动机排放数据库 (EEDB) 和现场测量的综合数据.
- 通过将预测数据与测量排放数据进行比较,验证了模型的准确性.
主要成果:
- 该CNN模型在地面 (R2>0.95) 和巡航 (R2>0.9) 阶段都显示了NOx排放的高预测精度.
- 该模型的性能超过了波音燃料流量方法2 (BFFM2) 等传统方法.
- 通过使用SAF的飞机实现了NOx排放的准确预测.
结论:
- 开发的基于CNN的模型提供了一个强大的工具,用于预测整个飞行范围内的航空NOx排放.
- 这有助于建立更准确的航空排放清单,减少气候和空气质量影响评估中的不确定性.
- 该模型支持对航空环境影响的研究,包括使用可持续航空燃料.
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