Robust 4D climate-optimal aircraft trajectory planning under weather-induced uncertainties: Free-routing airspace

被引:9
作者
Simorgh, Abolfazl [1 ]
Soler, Manuel [1 ]
Dietmueller, Simone [2 ]
Matthes, Sigrun [2 ]
Yamashita, Hiroshi [2 ]
Castino, Federica [3 ]
Yin, Feijia [3 ]
机构
[1] Univ Carlos III Madrid, Dept Aerosp Engn, Leganes 28911, Spain
[2] Inst Phys Atmosphare, Deutsch Zentrum Luft & Raumfahrt, Oberpfaffenhofen, Germany
[3] Delft Univ Technol, Fac Aerosp Engn, Delft, Netherlands
关键词
Climate change; Aircraft trajectory optimization; Non-CO2 climate-sensitive areas; Meteorological uncertainty; Robustness; Robust optimal control; IMPACT; CONTRAILS; AVIATION; SIMULATION; OPTIONS;
D O I
10.1016/j.trd.2024.104196
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The non-CO 2 climate impact of aviation strongly relies on the atmospheric conditions at the time and location of emissions. Therefore, it is possible to mitigate their associated climate impact by planning trajectories to re-route airspace areas with significant climate effects. Identifying such climate-sensitive regions requires specific weather variables. Inevitably uncertain weather forecasts can lead to inefficient aircraft trajectories if not accounted for within flight planning. The current study addresses the problem of generating robust climate-friendly flight plans under meteorological uncertainty characterized using the ensemble prediction system. We introduce a framework based on the concept of robust tracking optimal control theory to formulate and solve the proposed flight planning problem. Meteorological uncertainty effects on aircraft performance variables are captured using the formulated ensemble aircraft dynamical model and controlled by penalizing the performance index variance. Case studies show that the proposed approach can generate climate-optimized trajectories with minimal sensitivity to weather uncertainty.
引用
收藏
页数:35
相关论文
共 49 条
[1]  
APPLEMAN H, 1953, B AM METEOROL SOC, V34, P14, DOI DOI 10.1175/1520-0477-34.1.14
[2]   Conflict assessment and resolution of climate-optimal aircraft trajectories at network scale [J].
Baneshi, Fateme ;
Soler, Manuel ;
Simorgh, Abolfazl .
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2023, 115
[3]   The quiet revolution of numerical weather prediction [J].
Bauer, Peter ;
Thorpe, Alan ;
Brunet, Gilbert .
NATURE, 2015, 525 (7567) :47-55
[4]  
Betts JT, 2010, ADV DES CONTROL, P411
[5]   IMPACT OF AVIATION ON CLIMATE FAA's Aviation Climate Change Research Initiative (ACCRI) Phase II [J].
Brasseur, Guy P. ;
Gupta, Mohan ;
Anderson, Bruce E. ;
Balasubramanian, Sathya ;
Barrett, Steven ;
Duda, David ;
Fleming, Gregg ;
Forster, Piers M. ;
Fuglestvedt, Jan ;
Gettelman, Andrew ;
Halthore, Rangasayi N. ;
Jacob, S. Daniel ;
Jacobson, Mark Z. ;
Khodayari, Arezoo ;
Liou, Kuo-Nan ;
Lund, Marianne T. ;
Miake-Lye, Richard C. ;
Minnis, Patrick ;
Olsen, Seth ;
Penner, Joyce E. ;
Prinn, Ronald ;
Schumann, Ulrich ;
Selkirk, Henry B. ;
Sokolov, Andrei ;
Unger, Nadine ;
Wolfe, Philip ;
Wong, Hsi-Wu ;
Wuebbles, Donald W. ;
Yi, Bingqi ;
Yang, Ping ;
Zhou, Cheng .
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2016, 97 (04) :561-583
[6]  
Campbell S., 2008, AIAA GUIDANCE NAVIGA, P6515, DOI DOI 10.2514/6.2008-6515
[7]  
Castino F., 2023, Geosci. Model Dev. Discuss, P1
[8]  
Celis C, 2014, J AEROSP TECHNOL MAN, V6, P29
[9]   Can we reliably assess climate mitigation options for air traffic scenarios despite large uncertainties in atmospheric processes? [J].
Dahlmann, K. ;
Grewe, V. ;
Froemming, C. ;
Burkhardt, U. .
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2016, 46 :40-55
[10]   Numerical challenges in the use of polynomial chaos representations for stochastic processes [J].
Debusschere, BJ ;
Najm, HN ;
Pébay, PP ;
Knio, OM ;
Ghanem, RG ;
Le Maître, OP .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2004, 26 (02) :698-719