4 results
Now showing1 - 4 of 4
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Mitigating the climate impact of aviation by operational means - a comparative study for different weather situations(International Council of the Aeronautical Sciences, 2024); ; ; Aviation’s climate impact in terms of non-CO2 effects can be mitigated by operational trajectory adjustments. However, their effectiveness and efficiency varies significantly for different routes as well as meteorological conditions. Therefore, this study aims for a comparable assessment of different trajectory adaptation approaches under different weather conditions. For this purpose, detailed trajectory calculations are combined with climate sensitivity grids based on algorithmic climate change functions in order to assess variations in climate mitigation potentials. Significant mitigation potentials for vertical, lateral and temporal adaptation approaches are demonstrated and varying mitigation potentials in dependence of the meteorological situation are identified. For instance, higher mitigation potentials can be observed in December compared to June and for missions of higher contrail intensity.Publicationtype: Conference Paper27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0(Copernicus Publications, 2023-08-02); ; ; ; Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.Publicationtype: Journal ArticleScopus© Citations 7 30 65 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0(2023-07-06); ; ; ; The 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.Publicationtype: Journal ArticleScopus© Citations 5 29 48 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Mitigation of aviation’s climate impact through robust climate optimized trajectories in intra-european airspace(ICAS, 2022-09); ; ; ; Aircraft trajectories are currently flown and optimized to reduce operating costs, keeping engine CO2-emissions from burnt fuel at a minimum by following fuel optimized routes under consideration of wind. However, research has shown that the location and time of non-CO2 emissions such as NOx, water vapor or the formation of contrail cirrus contribute to about two thirds of aviation’s induced climate impact [1]. Consequently, one option to reduce this impact on a short time horizon is operational measures that aim to optimize aircraft trajectories with regard to climate impact by avoiding atmospheric regions that are especially sensitive to non-CO2 emissions from aviation. For this purpose, the effects of individual emission species need to be quantified in order to assess the mitigation potential by climate-optimized routing. For this reason, multi-dimensional algorithmic climate change functions, which allow for the quantification of the climate impact of emissions, based on meteorological parameters which a e available from weather forecast data is used. These algorithmic climate change functions are integrated into the cost functional of a trajectory planning algorithm which is based on an optimal control approach and applied in order to estimate climate optimized aircraft trajectories trading climate impact reduction against cost increase. Since the climate impact and therefore the algorithmic climate change functions are highly dependent on the prevailing atmospheric conditions, particularly the formation of contrail cirrus, weather prediction uncertainties are considered in order to determine robust eco efficient trajectories. Within this study, the methodology and optimization applied to determine such a robust solution are presented and results are analyzed for an exemplary intra-European flight route.Publicationtype: Conference PaperScopus© Citations 1 116 204