LM
L. Madi
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2 records found
1
Improvements in air traffic management (ATM) operations offer a faster pathway to reducing aviation emissions compared to long-term technological solutions. Existing ATM performance frameworks rely on proxy indicators that fail to account for wind effects and vertical profile optimization. This research developed a fuel-based flight inefficiency quantification framework utilizing the open-source OpenAP aircraft performance model and OpenAP.top trajectory optimizer, incorporating wind effects and individual initial mass estimation. The methodology was applied to over 200,000 commercial operations at Amsterdam Airport Schiphol in 2024, comparing executed trajectories against wind-optimal references at city-pair and Flight Information Region (FIR) levels. Results show that strategic inefficiency consistently represents the larger component (7-25% for most routes), while tactical inefficiency is predominantly negative, indicating execution improvements through ATC interventions and wind exploitation. FIR analysis reveals distinct phase characteristics: horizontal inefficiency accounts for approximately 90% of climb inefficiency, while descent operations show vertical inefficiency contributing the larger share with greater seasonal variation in horizontal components. Total relative inefficiencies differ between phases (climb: 9-14%; descent: 24-30%), with the magnitude difference amplified by lower baseline fuel consumption during descent. Wind integration proved essential; for an example flight, omitting wind nearly doubled city-pair inefficiency while underestimating descent inefficiency. Initial mass estimation uncertainty propagates through the analysis and reported values represent conservative upper bounds with actual relative total inefficiencies potentially 15-30% lower. The framework provides researchers and air navigation service providers with tools for environmental performance monitoring and quantitative baselines for evaluating ATM initiatives.
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Improvements in air traffic management (ATM) operations offer a faster pathway to reducing aviation emissions compared to long-term technological solutions. Existing ATM performance frameworks rely on proxy indicators that fail to account for wind effects and vertical profile optimization. This research developed a fuel-based flight inefficiency quantification framework utilizing the open-source OpenAP aircraft performance model and OpenAP.top trajectory optimizer, incorporating wind effects and individual initial mass estimation. The methodology was applied to over 200,000 commercial operations at Amsterdam Airport Schiphol in 2024, comparing executed trajectories against wind-optimal references at city-pair and Flight Information Region (FIR) levels. Results show that strategic inefficiency consistently represents the larger component (7-25% for most routes), while tactical inefficiency is predominantly negative, indicating execution improvements through ATC interventions and wind exploitation. FIR analysis reveals distinct phase characteristics: horizontal inefficiency accounts for approximately 90% of climb inefficiency, while descent operations show vertical inefficiency contributing the larger share with greater seasonal variation in horizontal components. Total relative inefficiencies differ between phases (climb: 9-14%; descent: 24-30%), with the magnitude difference amplified by lower baseline fuel consumption during descent. Wind integration proved essential; for an example flight, omitting wind nearly doubled city-pair inefficiency while underestimating descent inefficiency. Initial mass estimation uncertainty propagates through the analysis and reported values represent conservative upper bounds with actual relative total inefficiencies potentially 15-30% lower. The framework provides researchers and air navigation service providers with tools for environmental performance monitoring and quantitative baselines for evaluating ATM initiatives.
Bachelor thesis
(2022)
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V.D. van Deursen, M.T. Hitzerd, Wikash Chitoe, Pooh Laohamethanee, J.A. Evans, N.F. Gebhardt, A.C. Papuc, L. Madi, Dries Allaerts, R. Saathof, M. Rehbein, S. Hamaza
The goal of this report is to outline the sub-system design of the local sensing system chosen as the final concept in [1], to satisfy the mission need statement: measure the atmospheric conditions with full three-dimensional coverage of a wind farm to optimize its operational performance and control. This statement is derived from the need to improve the control and performance of wind farms through more informed processes and decisions, a task that meteorological masts would usually take on. However, the providable coverage is very low in comparison to the one a UAV based system could provide. UAVs have the potential to significantly increase the measurement coverage around an entire wind farm and in turn return to the user more valuable data. To approach the finding of a solution to this problem, the project was divided into four: planning, concept definition, concept exploration and detailed design. From the first two phases came unique concepts exploring remote and local sensing options, combined with a range of UAV types including hybrid, fixed-wing and rotor. Through a detailed trade-off process and sensitivity analysis, the agreed upon final solution came to be a local sensing concept that makes use of many hybrid drones. In the fourth and final phase, where we now find ourselves, the detailed concept is unpacked and designed into a marketable system that is capable of satisfying the underlying MNS. In this stage the design was split into three design groups: UAV design, ground station design, swarm design.
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The goal of this report is to outline the sub-system design of the local sensing system chosen as the final concept in [1], to satisfy the mission need statement: measure the atmospheric conditions with full three-dimensional coverage of a wind farm to optimize its operational performance and control. This statement is derived from the need to improve the control and performance of wind farms through more informed processes and decisions, a task that meteorological masts would usually take on. However, the providable coverage is very low in comparison to the one a UAV based system could provide. UAVs have the potential to significantly increase the measurement coverage around an entire wind farm and in turn return to the user more valuable data. To approach the finding of a solution to this problem, the project was divided into four: planning, concept definition, concept exploration and detailed design. From the first two phases came unique concepts exploring remote and local sensing options, combined with a range of UAV types including hybrid, fixed-wing and rotor. Through a detailed trade-off process and sensitivity analysis, the agreed upon final solution came to be a local sensing concept that makes use of many hybrid drones. In the fourth and final phase, where we now find ourselves, the detailed concept is unpacked and designed into a marketable system that is capable of satisfying the underlying MNS. In this stage the design was split into three design groups: UAV design, ground station design, swarm design.