Aeronautics and Astronautics - Ph.D. / Sc.D.
http://hdl.handle.net/1721.1/7766
2018-06-21T18:47:47ZStatistical modeling of aircraft engine fuel burn
http://hdl.handle.net/1721.1/115658
Statistical modeling of aircraft engine fuel burn
Chati, Yashovardhan Sushil
Fuel burn is a key driver of aircraft performance, and contributes to airline costs and aviation emissions. While the trajectory (ground track) of a flight can be observed using surveillance systems, its fuel consumption is generally not disseminated by the operating airline. Emissions inventories and benefits assessment tools therefore need models that can predict the fuel flow rate profile and fuel burn of a flight, given its trajectory data. Most existing fuel burn estimation tools rely on an architecture that is centered around the Base of Aircraft Data (BADA), an aircraft performance model developed by EUROCONTROL. Operational data (including trajectory data) are generally processed in order to generate the inputs needed by BADA, which then provides an estimate of the fuel flow rate and fuel burn. Although a versatile tool that covers a large number of aircraft types, BADA makes several assumptions that are not representative of real-world operations. Consequently, the reliance on BADA results in errors in the fuel burn estimates. Additionally, existing fuel burn modeling tools provide deterministic predictions, thereby not capturing the operational variability seen in practice. This thesis proposes an alternative model architecture that enables the development of data-driven, statistical models of fuel burn. The parameters of interest are the instantaneous fuel flow rate (that is, the mass of fuel consumed per unit time) and the fuel burn (cumulative mass of fuel consumed over a particular phase or the entire trajectory). The new model architecture uses supervised learning algorithms to directly map aircraft trajectory variables to the fuel flow rate, and subsequently, fuel burn. The models are trained and validated using operational data from flight recorders, and therefore reflect real-world operations. A physical understanding of aircraft and engine performance is leveraged for feature selection. An important characteristic of statistical methods is that they provide both estimates of mean values, as well as predictive distributions reflecting the variability and uncertainty. Locally expert models are developed for each aircraft type and for each of the flight phases. The Bayesian technique of Gaussian Process Regression (GPR) is found to be well-suited for modeling fuel burn. The resulting models are found to be significantly better than state-of-the-art aircraft performance models in predicting the fuel flow rate and fuel burn of a trajectory, giving up to a 63% improvement in total airborne fuel burn prediction over the BADA model. Finally, the Takeoff Weight (TOW) of an aircraft is recognized as an important variable for determining the fuel burn. The thesis therefore develops and evaluates a methodology to estimate the TOW of a flight, using trajectory data from its takeoff ground roll. The proposed statistical models are found to result in up to a 76% smaller error than the Aircraft Noise and Performance (ANP) database, which is used currently for TOW estimation.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 169-177).
2018-01-01T00:00:00ZNetwork navigation with scheduling
http://hdl.handle.net/1721.1/115653
Network navigation with scheduling
Wang, Tianheng, Ph. D. Massachusetts Institute of Technology
Network navigation is a promising paradigm for enabling location-awareness in dynamic wireless networks. A wireless navigation network consists of agents (mobile with unknown locations) and anchors (possibly mobile with known locations). An agent can estimate its locations based on inter- and intra-node measurements, as well as prior knowledge. In the presence of limited wireless resources, only a subset rather than all of the node pairs can perform inter-node measurements at a time. The procedure of selecting node pairs at different time instants for inter-node measurements, referred to as network scheduling, affects the time evolution of agents' localization errors. The key to achieve high navigation accuracy and efficient channel usage is to maximize the benefit from agents' inter-node measurements. Therefore, it is critical to design scheduling algorithms that decide for each agent with whom and when to perform inter-node measurements. This thesis introduces situation-aware scheduling that exploits network states to adaptively schedule agents' inter-node measurements. In particular, an analytical framework is developed to determine the effects of scheduling strategies and network settings on the localization error evolution. Furthermore, efficient and distributed situation-aware scheduling algorithms tailored for wireless navigation networks are designed, leading to high navigation accuracy and efficient channel usage. The first part of the thesis develops an analytical framework to determine the localization error evolution as a function of scheduling algorithms and network settings. In particular, both sufficient and necessary conditions for the boundedness of the error evolution are provided. Furthermore, opportunistic and random situation-aware scheduling strategies are proposed, and bounds on the corresponding time-averaged network localization errors are derived. These strategies are proved to be optimal in terms of the error scaling with the number of agents. Finally, the navigation accuracy is shown to be improved by sharing the wireless resources among multiple measurement pairs instead of allocating all the resources to a single pair at a time. The second part of the thesis designs efficient slotted and unslotted situation-aware scheduling algorithms tailored for wireless navigation networks based on the analytical results from the first part. The algorithm parameters, such as access probabilities and access rates, are optimized based on bounds for the time-averaged network localization error (NLE). The proposed algorithms lead to significant performance improvement compared with scheduling algorithms from wireless communication networks. The third part of the thesis develops a framework for the design of random-access-based distributed and asynchronous scheduling algorithms for wireless navigation networks, in which the channel access probabilities are optimized based on the evolution of agents' localization errors. The proposed algorithm achieves higher navigation accuracy and more efficient channel usage than the commonly used carrier sensing multiple access (CSMA) algorithm from wireless communication networks, at the cost of minimal communication overhead and computational complexity. The performance improvement is shown via numerical and experimental results. The contributions of this thesis provide a framework for the analysis and design of scheduling algorithms for wireless navigation networks, leading to high-accuracy, efficient, and flexible network navigation.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 155-164).
2018-01-01T00:00:00ZThe causes and consequences of divergence between the air traffic controller state awareness and actual system state
http://hdl.handle.net/1721.1/115647
The causes and consequences of divergence between the air traffic controller state awareness and actual system state
Abel, Brandon R. (Brandon Ross)
Divergence is an inconsistency between the human's system state awareness and the actual system state. This research investigated divergence potential in air traffic controllers and identified controller divergence causes and consequences. Based on this investigation, approaches to minimize controller divergence and its consequences were identified for current air traffic control systems and future systems where unmanned aircraft will be integrated. Prior studies identified pilot divergence as a factor in several recent aircraft accidents and could be a factor for controllers. The future addition of unmanned aircraft in national airspace is a significant change which will affect the pilot and controller relationship and presents an opportunity to consider divergence before procedures are developed. To understand how to minimize divergence and its consequences, this research developed a divergence cause and consequence framework and a cognitive process framework. The cause and consequence framework was developed using established risk analysis methods. The cognitive process framework was developed using established cognitive process and human error approaches. This research refined these frameworks and demonstrated their utility in an investigation of historical air traffic control accidents. They were then used to identify divergence vulnerabilities in a future unmanned aircraft-integrated national airspace. Air traffic control cases were analyzed between 2011 and 2015 using the framework to understand causes and consequences of controller divergence. Twenty-seven (sixty-four percent) of these cases contained controller divergence contributing to the hazardous consequence. Although divergence causes and states varied, the most common event sequence included a diverged controller inducing an aircraft-to-aircraft conflict. These cases provided insight for system mitigations to reduce divergence causes and the consequentiality should it occur. The potential emergence of controller divergence with the integration of unmanned aircraft in national airspace was then investigated. Field studies of controllers experienced managing unmanned aircraft identified important differences between manned and unmanned aircraft. The framework was then used to analyze these potential divergence vulnerabilities. Observables, specifically intent, appear more challenging to perceive yet crucial for controller projection of unmanned aircraft position due to their lack of onboard human perception, lost link, and automated operations. Hazardous consequences may be more likely due to the inability for unmanned aircraft to provide mitigations.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.; Cataloged from PDF version of thesis. "February 2018."; Includes bibliographical references (pages 177-195).
2018-01-01T00:00:00ZGenerative multi-robot task and motion planning over long horizons
http://hdl.handle.net/1721.1/115594
Generative multi-robot task and motion planning over long horizons
Fernández González, Enrique, Ph. D. Massachusetts Institute of Technology
The state of the art practice in robotics planning is to script behaviors manually, where each behavior is typically precomputed in advance. However, in order for robots to be able to act robustly and adapt to novel situations, they need to be able to plan sequences of behaviors and activities autonomously. Since the conditions and effects of these behaviors are tightly coupled through time, state and control variables, many problems require that the tasks of activity planning and trajectory optimization are considered together. There are two key issues underlying effective hybrid activity and trajectory planning: the sufficiently accurate modeling of robot dynamics and the capability of planning over long horizons. Hybrid activity and trajectory planners that employ mixed integer programming within a discrete time formulation are able to accurately model complex dynamics for robot vehicles, but are often restricted to relatively short horizons. On the other hand, current hybrid activity planners that employ continuous time formulations can handle longer horizons but they only allow actions to have continuous effects with constant rate of change, and restrict the allowed state constraints to linear inequalities. This greatly limits the expressivity of the problems that these approaches can solve. In this work we present Scotty, a planning system for hybrid activity and trajectory planning problems. Unlike other continuous time planners, Scotty can solve a broad class of expressive robotic planning problems by supporting convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. In order to efficiently generate practical plans for coordinated mobile robots over long horizons, our approach employs recent methods in convex optimization combined with methods for planning with relaxed planning graphs and heuristic forward search. The contributions of this thesis are threefold. First, we introduce a convex, goal-directed scheduling and trajectory planning problem. To solve this problem, we present the ScottyConvexPath planner, which reformulates the problem as a Second Order Cone Program (SOCP). Our formulation allows us to efficiently compute robot trajectories with first order dynamics over long horizons. While straightforward formulations are not convex, we present a convex model that does not require state, control or time discretization. Second, we introduce the ScottyActivity planner, a state of the art hybrid activity and trajectory planner that interleaves heuristic forward search with delete relaxations and consistency checks using our convex model. Finally, we present ScottyPath, a qualitative state plan planner that computes control and obstacle-free state trajectories for robots in order to satisfy the temporally extended goals and constraints that ScottyActivity imposes. ScottyPath finds obstacle-free paths in which all robots are guaranteed to always remain within obstacle-free safe regions, which are computed in advance. We introduce several new robotic planning domains, that we use to evaluate the scalability of our planning system and compare the performance of our approach against other prior methods. Our results show that ScottyActivity performs similarly to other state of the art heuristic forward search activity planners, while solving much more expressive robotic planning problems. On the other hand, ScottyPath can generate obstacle-free paths where robots are contained in obstacle-free convex regions more than two orders of magnitude faster than alternative mixed-integer approaches.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 291-299).
2018-01-01T00:00:00Z