MS, University of Cincinnati, 2005, Engineering : Aerospace Engineering

Due to the runway threshold and airport capacity constraints, aircraft are often required to delay their arrival time when they are approaching the TRACON (Terminal Radar Approach Control) area to meet separation requirements and to ensure safety. This is particularly true in the US in the northeast corridor, where sectors are small, with shorter controllable time, and involving very complex and heavy traffic flows. In this situation, downstream schedule constraints may be passed upstream, most likely across multiple ARTCCs (Air Route Traffic Control Centers) and multiple sectors. More sectors may be needed to absorb the required delay. The technical issue for delaying aircraft over extended region is that uncertainties in flight time, and the rather close tolerance on final spacing, make delay predictions far into the future rather suspect. This paper provides a delay strategy that the problem of distributing delay across multiple sectors is addressed as a discrete optimal control problem. Game theory, coupled with dynamic programming (DP) is used in this research to give an optimal solution for the delay controls in each sector. In this application the sector delay is chosen to minimize a performance index and the uncertainty is viewed as an adversary trying to maximize the performance index. This DP approach is capable of creating a favorable delay distribution solution and the solution is fuel efficient. It is easy for controller to implement because the algorithm is computationally efficient, the method can quickly reallocate the delay by adjusting the model parameters to provide a robust solution. As currently formulated the DP algorithm ensures only separation at the terminal fix. However, at several intermediate points, the traffic may merge into a single stream from several directions. An algorithm is developed to integrate the DP algorithm so as to solve the intermediate merging conflict as well as ensuring terminal separation. The validity of this mechani (open full item for complete abstract)

Committee: Dr. Gary Slater (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2012, Industrial and Systems Engineering

Departure demand routinely exceeds capacity at several airports in the United States. Under traditional “first-come, first-served” approaches to airport surface management, demand exceeding capacity can cause longer departure queues than necessary to maintain efficient traffic flow. Long queues can lead to longer taxi out times and greater fuel burn than necessary, and can increase uncertainty and limit flexibility for flight operators while increasing workload for air traffic control personnel. Departure metering is one alternative approach that controls access to the active movement area relative to expected departure capacity and the desired number of aircraft in the departure queue (or similar measures). While the main goal is to control the number of aircraft in the departure queue, metering also can increase flexibility, reduce emissions, and improve information about the time a flight is likely to take off. Managing a departure metering procedure is a new role that is an example of a distributed adaptive planning task. This research examined human-centered design concepts for supporting people responsible for such tasks. In particular, the project developed information requirements and prototype displays to support a human agent(s) responsible for managing a departure metering procedure. These information requirements are intended to support proactive efforts to adapt a surface management plan under evolving conditions, appropriately modifying the plan, and scheduling implementation of the new plan. Departure metering procedure management requires re-planning in response to events that impact the departure process (such as an unexpected temporary runway closure). It also may require adapting the plan before any change in the departure process takes place and when information indicating the trajectory of the departure process is uncertain (such as a forecast change in weather conditions). Rather than always implementing the new plan immediately, a person may s (open full item for complete abstract)

Committee: Philip Smith PhD (Advisor); David Woods PhD (Committee Member); Emily Patterson PhD (Committee Member) Subjects: Engineering; Industrial Engineering; Systems Design; Transportation

Doctor of Philosophy, The Ohio State University, 2024, Industrial and Systems Engineering

Sociotechnical systems in safety-critical domains are distributed and contain interdependencies between the different elements, including human and automated roles that need to coordinate and synchronize their activities with dynamic events in the environment. The advancement of technology and the introduction of machines capable of acting at a higher level of autonomy has increased the complexity of such Distributed Work Systems (DWSs). An envisioned DWS is described by a set of static paper-based documents and will be deployed in the next few years. The short-range low-altitude air mobility system is one very good example of an envisioned DWS. Interactions between human and automated roles and their environment are dynamic, evolve, and change over time, causing emergent effects like taskload peaks and coordination breakdowns. A well-designed DWS will be able to keep pace with the work environment dynamics (like the dynamics of aircraft governed by laws of flight in a short-range low-altitude air mobility system) and succeed in responding to the disturbance. This creates the need to understand the dynamics of envisioned DWS, such as how a DWS performs in high-paced situations like anomaly response. Assessing the feasibility and robustness of an envisioned DWS comes with challenges: the physical system does not yet exist, its design and operations are often underspecified, and multiple versions may exist within a designer community about what future operations will look like. Therefore, as a part of this dissertation, an exploratory early-stage computational modeling and simulation technique is described and demonstrated to evaluate an envisioned DWS. Using functional modeling and computational simulation capabilities, the dissertation shows a technique that can help evaluate envisioned DWS by discovering things that are not uncovered by traditional normative simulations. The primary advantage of the technique is the ability to evaluate the dynamics of work in (open full item for complete abstract)

Committee: Martijn Ijtsma (Advisor); Michael Rayo (Committee Member); David Woods (Committee Member) Subjects: Industrial Engineering; Systems Design

PhD, University of Cincinnati, 2023, Engineering and Applied Science: Mechanical Engineering

The aim of this dissertation is to present the development of new approaches to air traffic management for vehicles operating within the national airspace. This dissertation examines air traffic management techniques for both manned commercial aircraft along with small unmanned aerial systems (sUAS). This dissertation starts with the development of a supervised learning algorithm to generate estimated time of arrival for commercial aircraft and covers a unique conflict prediction algorithm for sUAS which is based on near future path predictions. Both contributions assist in air traffic management by minimizing required human intervention. The dissertation culminates with the development of a novel predictive artificial potential field (PAPF) navigation system for sUAS. The PAPF relies upon Interacting Multiple Model (IMM) vehicle predictions and is designed to eliminate the need for human intervention as sUAS operate within the airspace by preemptively rerouting vehicles in real time based on conflicts that will likely happen in the near future. The PAPF system is run completely on-board the sUAS and does not require additional ground based resources. The first element of the dissertation covers the estimated time of arrival (ETA) predictor for commercial aircraft. The ETA predictor focused on aircraft landing at the Dallas Fort Worth airport and used historical flight and meteorological data to train a random forest regressor. The trained algorithm could be implemented to make use of real time data and estimate the remaining flight time once aircraft have entered within a 200 mile radius circle of the airport. The data used is readily available from airport facilities and the aircraft themselves via Automatic Dependent Surveillance-Broadcast (ADS-B). This work was supported by NASA Office of STEM Engagement and used historical flight data for aircraft landing at Dallas Fort Worth airport. The second element discussed in the dissertation consists of a (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); Michael Alexander-Ramos Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member); Tejas Puranik Ph.D. (Committee Member); Rajnikant Sharma Ph.D. (Committee Member); Zachariah Fuchs Ph.D. (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2016, Engineering and Applied Science: Aerospace Engineering

The National Air Space (NAS) can be easily described as a complex aviation system-of-systems that seamlessly works in harmony to provide safe transit for all aircraft within its domain. The number of aircraft within the NAS is growing and according the FAA, ``[o]n any given day, more than 85,000 flights are in the skies in the United States...This translates into roughly 5,000 planes in the skies above the United States at any given moment. More than 15,000 federal air traffic controllers in airport traffic control towers, terminal radar approach control facilities and air route traffic control centers guide pilots through the system''. The FAA is currently rolling out the Next Generation Air Transportation System (NextGen) to handle projected growth while leveraging satellite-based navigation for improved tracking. A key component to instantiating NextGen lies in the equipage of Automatic Dependent Surveillance-Broadcast (ADS-B), a performance based surveillance technology that uses GPS navigation for more precise positioning than radars providing increased situational awareness to air traffic controllers. Furthermore, the FAA is integrating UAS into the NAS, further congesting the airways and information load on air traffic controllers. The expected increase in aircraft density due to NextGen implementation and UAS integration will require innovative algorithms to cope with the increase data flow and to support air traffic controllers in their decision-making. This research presents a few innovative algorithms to support increased aircraft density and UAS integration into the NAS. First, it is imperative that individual tracks are correlated prior to fusing to ensure a proper picture of the environment is correct. However, current approaches do not scale well as the number of targets and sensors are increased. This work presents a fuzzy clustering design to hierarchically break the problem down into smaller subspaces prior to correlation. This approach provide (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Sundararaman Anand Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Bruce Walker Sc.D. (Committee Member) Subjects: Aerospace Materials

MS, Kent State University, 2015, College of Arts and Sciences / Department of Computer Science

Air transportation is an important part of the modern world. The demand for air travel is increasing every day. Despite the rapid growth of technology, air travel systems failed to grow at the same rate. The current system for air traffic control (ATC) is similar to the system used decades ago. With the current growth in demand for air traveling, ATC will not be able to handle this increase. Therefore, a better system needs to be implemented for ATC. One of the possible approaches is an automated ATC using associative processing. In this thesis we gathered information about how the current ATC system works, following the Federal Aviation Administration (FAA) rules. We also provide multiple resources (e.g., FAA documents) that provide a deeper understanding of ATC that will be useful in further studies of this topic. Also addressed are some issues of the current system such as cost, delays, and errors that may result due to an air traffic controller's actions and the limited capacity of air traffic controllers. We implemented a model to represent some of the aspects of the current ATC using associative processing instead of air traffic controllers. Automation of the handoff operation, collision detection, collision avoidance, and course correction are implemented. Our results indicate that the current bottlenecks involving air traffic control can be avoided by implementing this type of system in the future. The same system can be used for other applications such as controlling the flight of unmanned aerial vehicles for an automated package delivery system.

Committee: Johnnie Baker (Advisor); Paul Farrell (Committee Member); Arden Ruttan (Committee Member) Subjects: Aerospace Engineering; Aerospace Materials; Computer Science

MS, University of Cincinnati, 2003, Engineering : Aerospace Engineering

One key element in improving air traffic capacity and efficiency is the ability of the air traffic management system to predict accurately the future position of a vehicle along a standard route. Perhaps the most challenging problem in the current practice is to predict accurately the altitude profile of an aircraft during the ascent phase of flight. During the ascent, the vehicle performance is extremely sensitive to uncertainties in the vehicle weight, thrust and piloting procedures, none of which are currently known to air traffic controller whose job is to merge this departure aircraft into an en route stream of traffic. This thesis work investigates the use of adaptive control techniques to improve climb performance prediction. The aim is to accurately predict time to ‘top of climb' in the ascending phase of aircraft trajectory. The study is conducted in support of the CTAS air traffic control software, which is in development at NASA Ames Research Center in California. This investigation consists of a comparison between actual departure trajectories for MD80 type of aircraft and the results of MATLAB-based numerical simulation attempting to duplicate the measured energy rate and hence the trajectory during the climb phase. The technical approach taken in this thesis is to start with initial a priori models of aerodynamics and engine thrust. The thrust dependency is adapted based on the observed and calculated energy rates of the vehicle. The results indicate that this adaptive model can greatly improve climb performance prediction.

Committee: Dr. Gary Slater (Advisor) Subjects: Engineering, Aerospace