Aviation Projects

  • Project Code: 011 Optimal Reroutes for Severe Weather Avoidance
    Weather accounts for 70% of all the air traffic delays. So, by developing an efficient weather avoidance algorithm, there is a lot to be gained in terms of reducing the number and duration of delays. This research project was of course not the first one on this topic and several methods are already available. The first step was therefore to make a comparison between four of these algorithms: Matlab’s Optimization Toolbox, Dijkstra’s Algorithm, the Potential Field Method and the Optimal Control Approach. Dijkstra’s algorithm in fact shows a lot of similarity with the dynamic programming approach from [6] and the Optimal Control Approach is related to the approach taken in [13]. The other two algorithms are not common in weather avoidance problems, but do show the potential that is needed. It will therefore be interesting to take a closer look at these two algorithms. A trade-off was then carried out based upon the quality of the reroute, the robustness of the algorithm, its run time and versatility. This trade-off revealed that the Optimal Control Approach shows the most potential. More Information
  • Project Code: 09 Wind Optimal Routes for Long Distance City Pairs
    When flying extreme long distances, like from Singapore in Southeast Asia to New York in the United States, there are usually two things that are important to the travelers as well as to the operating airline. They both want to spend as less money and time as possible for such an extreme long distance flight. This means that it is for the benefit of all to find a route which optimizes the fuel consumption as well as the travel time. One solution for this can be offered by using the principle of Wind Optimal Routes. These routes represent the fastest and fuel efficient way to get from one to another city by aircraft, taking the wind into account but neglecting other weather hazards. On the sphere of the Earth the shortest way from one point to another according to mileage is called great circle route. It is only the fastest way as long as there is no wind, otherwise a wind optimized route can provide a better, in this sense faster and more fuel efficient, solution. More Information
  • Project Code: 08 Use of Third-party Aircraft Performance Tools in the Development of the Aviation Environmental Design Tool (AEDT)
    The FAA has recently updated the airport terminal area fuel consumption methods used in the Aviation Environmental Design Tool(AEDT). These updates are based on fi tting data from a commercial third party aircraft performance program (PIANO - Project Interactive Analysis and Optimization) to previously developed empirical equations. These algorithm updates have adequate fi delity in the terminal area to assist air transportation policy makers in weighing the costs and bene fits of competing environmental and economic demands. Comparison with Flight Data Recorder (FDR) information for in-service airline operations shows that the combination of new aircraft data with the methods of the FAA's models can accurately capture the fuel consumption consequences of di fferent terminal departure and arrival procedures within a reasonable level of uncertainty. This report presents the use of the software tool PIANO to develop a new source of aircraft performance and fuel consumption data for computing terminal area airplane fuel consumption that has been implemented in FAA's AEDT. The terminal area covers the departures and arrivals of flights till 10,000 feet above ground level. The data is developed using PIANO and applied in the AEDT algorithms to improve, with respect to the BADA 3.8 (EUROCONTROL's Base of Aircraft Data old method), the fuel consumption modeling in the terminal area. More Information
  • Project Code: 07 Learning Airline Behaviours and Preferences from Historical Airspace Flow Program Data
    In order to be able to design and simulate future collaborative Air Traffic Management (ATM) mechanisms that consider airline preferences, airline behaviours and preferences must be modelled. These models can also be useful in current operations for simulating the impact of a traffic management initiative before implementing it. This modelling may be complicated because different factors are involved for each flight and thus every flight has a different cost. However, airlines are reluctant to reveal their preferences because an airline’s competitors could potentially use this knowledge of objectives or costs to capture its market share or profits. Therefore the goal of this research is to learn about airline behaviours and preferences. One way to do this is to analyze how they have assigned flights to slots in Airspace Flow Programs (AFPs) in the past. More Information
  • Project Code: 06 Wake Vortex Modeling and Data Analysis
    This report describes a model that can simulate the velocity field due to vortices in 3D space and how this can be used to get more useful data like the heading and tilt angles of the vortex. The simulation model is in fact the necessary link between the already existing vortex velocity field models and the vortex data to be analyzed (the FRA data set in this case). Time limitation has forced several simplifications on the simulation model, of which the largest is the modeling of the vortex as a cylinder. Nevertheless, the model still is capable of simulating certain data sets reasonably well. By these certain data sets are meant those that were made during a period where the wind was blowing (nearly) parallel to the line of sensors. Next to that, the model gives insight into the effect that certain parameters have on the final response. Thus became apparent that especially the v-component gives a good indication of the heading and tilt angles and that the effect of the core-radius is rather small when it is larger than 8 meters and further from the sensor than 10 meters. More Information
  • Project Code: 05 Reduced Thrust Modelling in the INM Using Take-Off Performance Equations
    The Integrated Noise Model (INM) is a computer model designed to assess the potential aircraft noise impacts in the vicinity of airports. Its algorithms and frameworks are derived from those outlined by the Society of Automotive Engineers (SAE) Aerospace information report "Procedure for the Calculation of Airplane Noise in the Vicinity of Airports" (AIR-1845). The model makes use of aircraft and airport specific data, statistical coefficients, performance equations and weather conditions to determine noise levels produced as a result of specific operational modes, thrust settings and other environmental factors, using acoustical Noise-Power-Distance (NPD) data. Typical INM output is in the form of time-based noise contours or noise level data for pre-selected locations. More Information
  • Project Code: 04 Determination of Wake Vortex Orientation, Vortex Height, Axial Flow and Level Of Turbulence in the Frankfurt Dataset
    With the increase of air traffic over the last decades, the traffic congestion at airports becomes greater and greater. Much research is done to reduce separation standards to increase airports capacities. One important area of research looks into wake vortices and the possibilities to look for potentially dangerous situations caused by vortices. At the moment the Frankfurt dataset is under study by the Volpe National Transportation Systems Center. The dataset is a result of research done by the Deutsche Flugsicherung at Frankfurt International Airport (Germany) after a request from the airport to look into the possibilities of a Wake Vortex Warning System. Frankfurt has two parallel runways with a distance of 518m, which current separation standards have to be treated as one single runway. This has a great effect on Frankfurt’s capacity. More Information
  • Project Code: 01 Helicopter Fuel Burn Modeling in AEDT
    Based on the best of multiple fuel burn methods, a model is developed to evaluate fuel burn of helicopter flights, for different helicopters used for air tours in US National Parks. This will be implemented in the Aviation Environmental Design Tool (AEDT) of the FAA. The model is using the flight phase definition of the INM model already used in AEDT. More Information