Chen Longfei, Zhang Qian, Zhu Meiyin, Li Guangze, Chang Liuyong, Xu Zheng, Zhang Hefeng, Wang Yanjun, Zheng Yinger, Zhong Shenghui, Pan Kang, Zhao Yiwei, Gao Mengyun, Zhang Bin
State 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.
International Innovation Institute, Beihang University, Hangzhou 311115, China; School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
Sci Total Environ. 2024 Jun 15;929:172432. doi: 10.1016/j.scitotenv.2024.172432. Epub 2024 Apr 16.
In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R > 0.95) and cruise phases (R > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SYNOPSIS: The utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.
近年来，关于氮氧化物（NOx）排放的研究越来越多，航空NOx排放在巡航高度对环境的影响受到广泛关注。NOx可能在改变大气成分方面发挥关键作用，特别是在对流层上部的臭氧形成方面。目前，基于起降（LTO）循环的地面排放数据库较为全面，而由于成本过高以及与飞行中采样相关的不可避免的测量不确定性，高空排放数据稀缺。因此，有必要建立一个涵盖整个飞行包线（包括地面和巡航阶段）的全面的NOx排放数据库。这将能够全面评估航空NOx排放对气候和空气质量的影响。在本研究中，通过卷积神经网络（CNN）技术开发了一个预测模型。该模型可以预测由传统航空煤油或可持续航空燃料（SAF）提供燃料的涡轮风扇发动机和混合涡轮风扇发动机的地面和巡航NOx排放指数。该模型利用国际民用航空组织（ICAO）发布的发动机排放数据库（EEDB）中的数据以及在地面和巡航阶段进行的几次现场排放测量结果。通过比较测量数据和预测数据对模型进行了验证，结果表明其在地面阶段（R > 0.95）和巡航阶段（R > 0.9）都具有很高的预测准确性。这超过了传统的依赖燃料流量的预测模型，如波音燃料流量方法2（BFFM2）。此外，该模型能够以令人满意的准确性预测燃烧SAF的飞机的NOx排放，有助于开发更完整、准确的航空NOx排放清单，可为航空环境和气候研究提供依据。摘要：ANOEPM-CNN的应用为建立更精确的排放清单奠定了基础，从而减少了评估航空NOx排放对气候和空气质量影响时的不准确之处。
