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Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Ingeniería Aeroespaciales
dc.contributor.authorSimorgh, Abolfazl
dc.contributor.authorSoler Arnedo, Manuel Fernando
dc.contributor.authorGonzález Arribas, Daniel
dc.contributor.authorLinke, Florian
dc.contributor.authorBaumann, Sabine
dc.contributor.authorLührs, Benjamin
dc.contributor.authorMeuser, Maximilian M.
dc.contributor.authorDietmüller, Simone
dc.contributor.authorMatthes, Sigrun
dc.contributor.authorYamashita, Hiroshi
dc.contributor.authorYin, Feijia
dc.contributor.authorCastino, Federica
dc.contributor.authorGrewe, Volker
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2024-01-22T19:21:38Z
dc.date.available2024-01-22T19:21:38Z
dc.date.issued2023-07-06
dc.description.abstractThe climate impact of non-CO2 emissions, which are 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 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 optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in the operating cost.en
dc.description.sponsorshipFlyATM4E has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation program (grant no. 891317). The JU receives support from the European Union's Horizon 2020 research and innovation program and the SESAR JU members other than the union.en
dc.description.statusPublicadoes
dc.format.extent26
dc.identifier.bibliographicCitationSimorgh, A., Soler, M., González-Arribas, D., Linke, F., Lührs, B., Meuser, M. M., Dietmüller, S., Matthes, S., Yamashita, H., Yin, F., Castino, F., Grewe, V., and Baumann, S.: Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0. Geosci. Model Dev. (2023), 16, 3723–3748, (26p.). https://doi.org/10.5194/gmd-16-3723-2023en
dc.identifier.doihttps://doi.org/10.5194/gmd-16-3723-2023
dc.identifier.issn1991-959X
dc.identifier.publicationfirstpage3723
dc.identifier.publicationissue13
dc.identifier.publicationlastpage3748
dc.identifier.publicationtitleGeoscientific Model Developmenten
dc.identifier.publicationvolume16
dc.identifier.urihttps://hdl.handle.net/10016/39424
dc.identifier.uxxiAR/0000033718
dc.language.isoengen
dc.publisherCopernicus Publicationsen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/GA-891317es
dc.rights© Author(s) 2023.en
dc.rightsThis work is distributed under the Creative Commons Attribution 4.0 License.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaAeronáuticaes
dc.subject.ecienciaFísicaes
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherContrailsen
dc.subject.otherImpacten
dc.subject.otherOptionsen
dc.titleRobust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0es
dc.typeresearch articleen
dc.type.hasVersionVoRen
dspace.entity.typePublication
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