Neil Y. Chen

Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds

There are indications that persistent contrails can lead to adverse climate change, although the complete effect on climate forcing is still uncertain. A flight trajectory optimization algorithm with fuel and contrails models, which develops alternative flight paths, provides policy makers the necessary data to make tradeoffs between persistent contrails mitigation and aircraft fuel consumption. This study develops an algorithm that calculates wind-optimal trajectories for cruising aircraft while avoiding the regions of airspace prone to persistent contrails formation. The optimal trajectories are developed by solving a non-linear optimal control problem with path constraints. The regions of airspace favorable to persistent contrails formation are modeled as penalty areas that aircraft should avoid and are adjustable. The tradeoff between persistent contrails formation and additional fuel consumption is investigated, with and without altitude optimization, for 12 city-pairs in the continental United States. Without altitude optimization, the reduction in contrail travel times is gradual with increase in total fuel consumption. When altitude is optimized, a two percent increase in total fuel consumption can reduce the total travel times through contrail regions by more than six times. Allowing further increase in fuel consumption does not seem to result in proportionate decrease in contrail travel times.

Short-Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index

This paper describes a method to predict delay in the National Airspace System for durations of up to two hours. Various linear autoregressive model structures with exogenous inputs were implemented to perform the delay prediction. Current and forecast weather impacted traffic indices and air traffic volume were used as inputs to the system while the air traffic delay is the predicted output of the model. The refined methodology for generating the weather impacted traffic indices, together with the high-update-rate of Corridor Integrated Weather System is well suited for real-time delay prediction. An adaptive scheme was implemented to provide both weather-related and non-weather-related delay predictions depending on the weather and air traffic condition. The method will benefit air traffic management by facilitating the development of strategies to reduce delays, cancellations, and other costs during the day of operations in various weather conditions.

Tradeoff Between Contrail Reduction and Emissions in United States National Airspace

This paper describes a class of strategies for reducing persistent contrail formation with the capability of trading o between contrails and aircraft induced emissions. The concept of contrail frequency index is dened and used to quantify the contrail activities. The contrail reduction strategies reduce the contrail frequency index by altering aircraft’s cruising altitude with consideration to extra emissions. The strategies use a user-dened factor to trade o between contrail reduction and extra emissions. The analysis shows that contrails can be reduced with extra emissions and without adding congestion to airspace. For a day with high contrail activities, the results show that the maximal contrail reduction strategy can achieve a contrail reduction of 88%. When a trade-o factor is used, the strategy can achieve less contrail reduction while emitting less emissions compared to the maximal contrail reduction strategy. The user-dened trade-o factor provides a exible way to trade o between contrail reduction and extra emissions. Better understanding of the trade-os between contrails and emissions and their impact on the climate need to be developed to fully utilize this class of contrail reduction strategies. The strategies provide a starting point for developing operational policies to reduce the impact of aviation on climate.

Integration of Linear Dynamic Emission and Climate Models with Air Traffic Simulations

Future air traffic management systems are required to balance the conflicting objectives of maximizing safety and efficiency of traffic flows while minimizing the climate impact of aviation emissions and contrails. Integrating emission and climate models together with air traffic simulations improve the understanding of the complex interaction between the physical climate system, carbon and other greenhouse gas emissions and aviation activity. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability can be used to make trade-offs between extra fuel cost and reduction in global surface temperature change. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails and the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA’s Aviation Environmental Portfolio Management Tool for Impacts.

Benefits Analysis of Wind-Optimal Operations For Trans- Atlantic Flights

North Atlantic Tracks are trans-Atlantic routes across the busiest oceanic airspace in the world. This study analyzes and compares current flight-plan routes to wind-optimal routes for trans-Atlantic flights. The historical flight track data recorded by EUROCONTROL’s Central Flow Management Unit is merged with data from FAA’s Enhanced Traffic Management System to provide an accurate flight movement database containing the highest available flight path resolution in both systems. The combined database is adopted for airspace simulation integrated with aircraft fuel burn to simulate traffic within the Organized Track System (OTS). The fuel burn for the tracks in the OTS are compared with the corresponding quantities for the wind-optimized routes for a month to evaluate the potential benefits of flying wind-optimal routes in North Atlantic Airspace. The potential fuel savings depend on existing inefficiencies in current flight plans, atmospheric conditions and location of the city-pairs. The potential benefits are compared with actual flight tests that have been conducted since 2010 between a few city-pairs in the trans-Atlantic region to improve fuel consumption.

Weather-Weighted Periodic Auto Regressive Models for Sector Demand Prediction

This paper describes a class of weather-weighted periodic auto-regressive sector demand prediction models. The periodic auto-regressive model captures both the mid-term (thirty minutes to two hours) trend based on the historical data and the short-term (less than thirty minutes) transient response based on recent observations. For severe weather days, the model uses three-dimensional weather information, both storm locations and echo tops, to form a weather factor to adjust the predictions. Unlike traditional trajectory-based sector demand prediction methods, which predict the behavior of the National Airspace System adequately for short durations of up to twenty minutes and are vulnerable to the weather uncertainties, this class of models provides reliable short to mid-term sector demand predictions and account for the weather uncertainty.

Prediction and Use of Contrail Frequency Index for Contrail Reduction Strategies

Methods have been proposed to reduce aircraft-induced contrails by on-board sensing and strategic planning. This paper describes a class of indices that predict potential aircraft-induced contrail formations one to six hours in advance. The indices can be used to identify air traffic control centers and altitudes with high potential for contrail formation. The results show that the index is affected more by the changing atmospheric conditions than by small daily variations in the nominal traffic. The analysis shows that the one-hour predicted contrail frequency index is highly correlated with the actual contrail frequency, with an average correlation coefficient of 0.85. The correlation coefficient is lower with longer prediction time, down to 0.52 for six-hour prediction. The average success rates for identifying air traffic control centers and altitudes with high contrail frequency are as high as 83.47% for one-hour prediction.

A Linear Programming Approach to the Development of Contrail Reduction Strategies Satisfying Operationally Feasible Constraints

A class of strategies has been proposed to reduce contrail formation in the United States airspace. A 3D grid based on weather data is built and the cruising altitude level of aircraft is adjusted to avoid the persistent contrail potential area with the consideration to fuel-eciency. In this paper, the authors introduce a contrail avoidance strategy on 3D grid by considering additional operationally feasible constraints from an air trac controllers aspect. First, shifting too many aircraft to the same cruising level will make the miles-in-trail at this level smaller than the safety separation threshold. Furthermore, the high density of aircraft at one cruising level may exceed the manageable workload for the trac controller. Therefore, in our new model we restrict the number of total aircraft at each level. Second, the aircraft count variation for successive intervals can not be too drastic since the workload to manage climbing/descending aircraft is much larger than managing cruising aircraft. The contrail reduction problem is formulated as integer programming and the problem is shown to have the property of total unimodularity. Solving the corresponding relaxed linear programming with the simplex method provides an optimal and integral solution to the problem. Simulation results are provided to illustrate the methodology.

Fuel efficient strategies for reducing contrail formations in United States airspace

This paper describes a class of strategies for reducing persistent contrail formation in the United States airspace. The primary objective is to minimize potential contrail formation regions by altering the aircrafts cruising altitude in a fuel-efficient way. The results show that the contrail formations can be reduced significantly without extra fuel consumption and without adversely affecting congestion in the airspace. The contrail formations can be further reduced by using extra fuel. For the day tested, the maximal reduction strategy has a 53% contrail reduction rate. The most fuel-efficient strategy has an 8% reduction rate with 2.86% less fuel-burnt compared to the maximal reduction strategy. Using a cost function which penalizes extra fuel consumed while maximizing the amount of contrail reduction provides a flexible way to trade off between contrail reduction and fuel consumption. It can achieve a 35% contrail reduction rate with only 0.23% extra fuel consumption. The proposed fuel-efficient contrail reduction strategy provides a solution to reduce aviation-induced environmental impact on a daily basis.

Management-Action-Embedded Sector-Demand Prediction Models

airtrafficcontrolandairlineactions,andthataccountsforbothshortterm (less than 30 min) and midterm (30 min to 2 h) predictions. The model consists of two parts: the open-loop prediction and the TFM action model. The open-loop predictions, similar to the traditional methods, are used to determine the possibility of demand-capacity imbalances at a future time, and help decide whether to activate the TFMaction.TheTFMactionmodelsimulatesthedemandreduction caused by the TFM actions. The closed-loop prediction represents the net result of the open-loop prediction and the TFM actions. The periodic autoregressive model and its variants [7,8] were used to build the model. The model considers both historical traffic flows to capture the midterm trend and flows in the near past to capture the transientresponse.Inaddition,forsevereweathercases,theweatherimpacted TFM action was modeled using weather forecast information. The proposed model provides both open- and closed-loop sector-demand predictions. Open-loop prediction is adequate for short durations. When looking at predictions for long durations, open-loop models produce large errors due to their inability to capture traffic initiatives and airline actions during the planning period.Acombinationofclosed-loopandopen-loopmodelsprovide decision-makers the full range of traffic behavior. The remainder of the paper is organized as follows. Section II provides the sector-demand data and a description of the open- and closed-loop sector-demand prediction models. Next, in Sec. III, a weatherfactorisintroducedandtheTFMactionmodelthatconsiders weather is described. The results and performance of the models are demonstrated in Sec. IV. Finally, a summary and conclusions are presented in Sec. V.