Preprints
https://doi.org/10.5194/egusphere-2022-1010
https://doi.org/10.5194/egusphere-2022-1010
 
15 Nov 2022
15 Nov 2022
Status: this preprint is open for discussion.

Robust 4D Climate Optimal Flight Planning in Structured Airspace using Parallelized Simulation on GPUs: ROOST V1.0

Abolfazl Simorgh1, Manuel Soler1, Daniel González-Arribas1, Florian Linke2,3, Benjamin Lührs2, Maximilian M. Meuser2,3, Simone Dietmüller4, Sigrun Matthes4, Hiroshi Yamashita4, Feijia Yin5, Federica Castino5, Volker Grewe4,5, and Sabine Baumann4 Abolfazl Simorgh et al.
  • 1Department of Aerospace Engineering, Universidad Carlos III de Madrid, Madrid, Spain
  • 2Hamburg University of Technology (TUHH), Hamburg, Germany
  • 3Deutsches Zentrum für Luft- und Raumfahrt, Air Transportation Systems, Hamburg, Germany
  • 4Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 5Faculty of Aerospace Engineering, Delft University of Technology, Delft, The Netherlands

Abstract. The climate impact of the non-CO2 emissions, being responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in the standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic opti- mization computationally fast. The effectiveness of our proposed strategy to plan robust climate optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with large contrails’ climate impact and a scenario with no formation of persistent contrails. It is shown that, for a night-time flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in cost.

Abolfazl Simorgh et al.

Status: open (until 10 Jan 2023)

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Abolfazl Simorgh et al.

Abolfazl Simorgh et al.

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Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a python library has been developed, which can be accessed using DOI: https://doi.org/10.5281/zenodo.7121862. It is shown that the developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.