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Daniel Guggenheim School of Aerospace Engineering

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search.filters.applied.f.advisor: Clarke, John-Paul B. × End date: 2022 × Start date: 2020 × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Engineering × search.filters.applied.f.resourcesubtype: Dissertation ×

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Now showing 1 - 4 of 4
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A Methodology for the Design and Operational Safety Assessment of Unmanned Aerial Systems

2022-12-15 , Kendall, Andrew Paul

Efforts are underway to introduce Unmanned Aerial Systems (UAS) into routine cargo operations within the National Airspace System (NAS). Such systems have the potential to increase transport system flexibility by mitigating crew scheduling constraints and extending operations to remote locations. It is expected that any large UAS operating in the transport category must comply with Federal Aviation Regulations to achieve airworthiness certification for routine operations within the NAS. Regulations on the safety of equipment, systems, and installations require all failure conditions due to malfunctions, environmental events, and inadequate corrective action to be mitigated and shown to be extremely improbable. These system safety requirements are particularly relevant for a UAS as the ability of a Remote Pilot (RP) to detect and respond to risks is dependent on a Command and Control (C2) link. Failure conditions associated with the C2 link system require autonomy onboard the aircraft to supplement the RP in order to mitigate risk. A method for assessing the performance required from automation when the RP cannot adequately mitigate risks is needed to allow routine UAS operations. The problem of ensuring autonomous UAS safety requirements is addressed in this thesis through the development of a safety assessment methodology that can be applied during both system design and online operations. The contributions are as follows: • Safety Regulations are formulated as a chance-constraint satisfaction problem, requiring safety on the order of 1 accident per billion operations. Rare event estimation techniques based on Importance Sampling are proposed to assess safety subject to various sources of uncertainty. • Failure conditions can be due to both discrete events, such as system failures, and continuous state uncertainties, such as navigation errors and turbulence. A stochastic hybrid system model is proposed to handle the coupling between discrete and continuous states and estimate the distribution of aircraft trajectories that may result from a given set of system parameters, operational conditions, and decision parameters. • The final approach and landing phase of flight serves as a use case for the methodology. The safety assessment is applied to determine system design parameters required to passively mitigate risks. The methodology is extended to active risk mitigation during operations, where online safety assessments using updated observations are used to ensure decision options always exist that will satisfy safety requirements.

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Supersonic descent staging aerodynamic and performance analysis

2020-01-06 , Blette, David Joseph

Supersonic Retropropulsion (SRP) is one potential enabling technology to extend Mars entry, descent, and landing (EDL) capability beyond current Viking-era technological landed mass upper limits of 1 mT to human-class landed payloads requiring 20-40 mT. To utilize SRP for human Mars missions, it is necessary to perform supersonic descent vehicle staging to transform an entry vehicle from its hypersonic configuration to a configuration that enables the use of SRP. These reconfigurations may require jettisoning the vehicle aeroshell as debris during supersonic flight. The ejected debris present risk to catastrophically recontact the primary descent vehicle during and after ejection. The flight dynamics of the ejected debris are complicated by supersonic interference aerodynamics between the primary descent vehicle and the ejected debris. The development of strategies to understand and mitigate debris recontact risk during supersonic descent vehicle reconfigurations is paramount to advancing SRP technology readiness level and therefore to enabling human missions to Mars. However, supersonic descent vehicle staging has not been flight proven and published research in the field is non-existent. The methodology developed in this thesis represents the first assessment of supersonic descent staging aerodynamic and performance analysis. The methodology addresses a gap in current analysis capability by providing the means to rapidly, quantitatively, and competitively evaluate a variety of proposed supersonic vehicle staging architectures to determine a subset of fittest candidates for further detailed investigation. Quantitative methodology output metrics consist of required ejection subsystem performance for a variety of jettison initiation conditions and jettison maneuver durations. The methodology also serves as a risk mitigation tool by enabling users to specify tolerable levels of recontact risk posed to the primary descent vehicle by the ejected debris. The methodology employs an iterative process between three primary analysis modules. The first module analyzes a piece of debris to determine the spatial flight envelope of the debris when it undergoes uncontrolled tumbling. The second module determines nominal flight trajectories that the debris must fly post-separation to ensure minimum offset distances are achieved between the primary vehicle and the debris before uncontrolled debris tumbling begins. The third module determines uncertainties about the nominal transit trajectories. The methodology iterates until successive solutions converge. The methodology is demonstrated on a 10x30 meter ellipsled entry vehicle utilizing a symmetric clam-shell supersonic aeroshell jettison maneuver for a reference human Mars mission. As a supplement to the methodology contribution, multi-fidelity modeling techniques are evaluated for applicability toward generating surrogate models of expensive interference aerodynamic responses by leveraging available inexpensive isolated aerodynamic response data. Multi-fidelity modeling techniques are found to improve the accuracy and k-fold cross-validation metrics of interference aerodynamics drag coefficient surrogate models as compared to single-fidelity modeling techniques. Multi-fidelity modeling techniques performed particularly well for models built from sparse sets of interference data.

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UNMANNED AERIAL VEHICLES: TRAJECTORY PLANNING AND ROUTING IN THE ERA OF ADVANCED AIR MOBILITY

2022-07-28 , Zeng, Fanruiqi

Advanced air mobility (AAM) is a revolutionary concept that enables on-demand air mobility, cargo delivery, and emergency services via an integrated and connected multimodal transportation network. In the era of AAM, unmanned aerial vehicles are envisioned as the primary tool for transporting people and cargo from point A to point B. This thesis focuses on the development of a core decision-making engine for strategic vehicle routing and trajectory planning of autonomous vehicles (AVs) with the goal of enhancing the system-wide safety, efficiency, and scalability. Part I of the thesis addresses the routing and coordination of a drone-truck pairing, where the drone travels to multiple locations to perform specified observation tasks and rendezvous periodically with the truck to swap its batteries. Drones, as an alternative mode of transportation, have advantages in terms of lower costs, better service, or the potential to provide new services that were previously not possible. Typically, those services involve routing a fleet of drones to meet specific demands. Despite the potential benefits, the drone has a natural limitation on the flight range due to its battery capacity. As a result, enabling the combination of a drone with a ground vehicle, which can serve as a mobile charging platform for the drone, is an important opportunity for practical impact and research challenges. We first propose a Mixed Integer Quadratically-constrained Programming driven by critical operational constraints. Given the NP-hard nature of the so called Nested-VRP, we analyze the complexity of the MIQCP model and propose both enhanced exact approach and efficient heuristic for solving the Nested-VRP model. We envision that this framework will facilitate the planning and operations of combined drone-truck missions and further improve the scalability and efficiency of the AAM system. Part II of the thesis focuses on the survivability reasoning and trajectory planning of UAVs under uncertainty. Maintaining the survivability of an UAV requires that it precisely perceives and transitions between safe states in the airspace. We first propose a methodology to construct a survivability map for an UAV as a function of the vehicle's maneuverability, remaining lifetime, availability of landing sites, and the volume of air traffic. The issue of trajectory planning under uncertainty has received a lot of attention in the robotics and control communities. Traditional trajectory planning approaches rely primarily on the premise that the uncertainty of dynamic obstacles is either bounded or can be statistically modeled. This is not the case in the urban environment, where the sources of uncertainty are diverse, and their uncertain behavior is typically unpredictable, making precise modeling impossible. Motivated by this, we present a receding horizon control method with innovative trajectory planning policies that enable dynamic updating of planned trajectories in the presence of partially known and unknown uncertainty. The findings of this study have significant implications for achieving safe aviation autonomy within the AAM system.

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MODEL-BASED APPROACH TO THE UTILIZATION OF HETEROGENEOUS NON-OVERLAPPING DATA IN THE OPTIMIZATION OF COMPLEX AIRPORT SYSTEMS

2021-12-15 , Nikoue, Harold

Simulation and optimization have been widely used in air transportation, particularly when it comes to determining how flight operations might evolve. However, with regards to passengers and the services provided to them, this is not the case in large part because the data required for such analysis is harder to collect, requiring the timely use of surveys and significant human labor. The ubiquity of always--connected smart devices and the rise of inexpensive smart devices has made it possible to continuously collect passenger information for passenger-centric solutions such as the automatic mitigation of passenger traffic. Using these devices, it is possible to capture dwell times, transit times, and delays directly from the customers. The data; however, is often sparse and heterogeneous, both spatially and temporally. For instance, the observations come at different times and have different levels of accuracy depending on the location, making it challenging to develop a precise network model of airport operations. The objective of this research is to provide online methods to sequentially correct the estimates of the dynamics of a system of queues despite noisy, quickly changing, and incomplete information. First, a sequential change point detection scheme based on a generalized likelihood ratio test is developed to detect a change in the dynamics of a single queue by using a combination of waiting times, time spent in queue, and queue-length measurements. A trade-off is made between the accuracy of the tests, the speed of the tests, the costs of the tests, and the value of utilizing the observations jointly or separately. The contribution is a robust detection methodology that quickly detects a change in queue dynamics from correlated measurements. In the second part of the work, a model-based estimation tool is developed to update the service rate distribution for a single queue from sparse and noisy airport operations data. Model Reference Adaptive Sampling is used in-the-loop to update a generalized gamma distribution for the service rates within a simulation of the queue at an airport’s immigration center. The contribution is a model predictive tool to optimize the service rates based on waiting time information. The two frameworks allow for the analysis of heterogeneous passenger data sources to enable the tactical mitigation of airport passenger traffic delays.

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