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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 10 of 138

Multi-mission sizing and selection methodology for space habitat subsystems

(Georgia Institute of Technology, 2019-12-11) Boutaud, Agathe Kathia

Future space missions aim to set up exploration missions in further space and establish settlements on other celestial bodies like the Moon or Mars. In this context, subsystem sizing and selection is crucial, not only because resource management is critical for the astronauts’ survival, but also because subsystems can account for more than 20% of the total mass of the habitat, so reducing their size can greatly impact the cost of the mission. A few tools already exist to size space habitat subsystems and assess their performance. However, these tools are either very high-fidelity and very slow or instantaneous but steady-state. Steady-state tools do not allow to take risks or mission variations into account and the dynamic, slower tools are less performing at helping stakeholders evaluate the impact of technology trade-offs because of their long running time. Faster sizing tools would also allow to implement additional capabilities, such as multi-mission sizing, which could be used to develop lunar or martian settlements. These tools are also used in the context of point-based design, which focuses on the development of one design throughout the process. Such approach can lead to a sub-optimal design because the selection of an alternative is made early in the design process, based on low-fidelity analyses. In addition, because the costs and design choices are committed early in the design process, requirements or design changes can have very significant cost consequences. This research proposes a new sizing capability, developed using HabNet [1], a dynamic space habitat simulation tool. It is faster than existing dynamic sizing tools and it allowed to develop a multi-mission sizing methodology using Design Space Exploration. Finally, leveraging the faster sizing tool developed to create surrogate models for the size of the elements in the habitat, it was shown that trade-off analyses can be used to support set-based design during the conceptual design phase. Consequently, the methodology proposed is faster than what is currently used to size and select space habitat subsystem technologies. It gives more insight to the user because it can perform instantaneous trade-offs. However, the quality of the surrogate models generated is not sufficient to validate the multi-mission sizing method and environment developed during this thesis. This methodology could be used as a basis for the development of a set-based design method for space habitats. Numerous capabilities, including the evaluation of the impact of disruptions or the level of uncertainty associated with the various alternatives considered, could be easily implemented and added to the existing tool.
X-ray Pulsar Navigation Instrument Performance and Scale Analysis

(Georgia Institute of Technology, 2019-12-06) Payne, Jacob Hurrell

This thesis investigates instruments for autonomous satellite navigation using measurements of X-ray emissions from millisecond pulsars. A manifestation of an instrument for this purpose, called the Neutron star Interior Composition Explorer (NICER), was launched to the International Space Station in 2017. The NICER instrument was designed to observe X-ray emissions from neutron stars for astrophysics research, and is out of scale in terms of volume, power consumption, mass and mechanical complexity to be useful for small satellite missions. This work surveys the range of existing X-ray observation missions to tabulate collecting areas, focal lengths, and optical configurations from milestone missions which describe the evolution of the state of the art in X-ray observatories. A navigation demonstration experiment, called the Station Explorer for X-ray Timing and Navigation Technology (SEXTANT), was conducted using the NICER instrument. The experimental performance observed from NICER through the SEXTANT navigation demonstration is compared to theoretical predictions established by existing formulations. It is concluded that SEXTANT benefits from soft band (0.3-4 keV) exposure to achieve better accuracy than predicted by theoretical lower bounds. Additionally, investigation is presented on the readiness of a navigation instrument for small satellites using compound refractive lensing (CRL) and derived designs. X-ray refraction achieves a much shorter focal length than grazing incidence optics at the expense of signal attenuation in the lens material. Performance estimates and previous experimental results are presented as a baseline for physical prototypes and hardware testing to support future development of a physical instrument. The technological hurdle that will enable this tool is manufacturing precise lenses on a 3-micron scale from materials like beryllium with low atomic mass. Recent X-ray concentrator concepts demonstrate progress towards an implementation that can support a CubeSat scale navigation instrument optimized for soft band (0.3-4 keV) X-rays.
Development of a Multidisciplinary Design Analysis Framework for Unmanned Electric Flying Wings

(Georgia Institute of Technology, 2019-12-03) Whitmore, William Valentin

Small-scale subsonic unmanned aerial vehicles have become common tools in both military and civil applications. A vehicle configuration of special interest is the flying wing (aka all-wing or tailless aircraft). This configuration can potentially reduce drag, increase structural efficiency, and decrease detectability. When combined with an electric propulsion system, it produces no observable emissions and possesses fewer maintenance issues. Unfortunately, strong couplings between disciplinary analyses hinder the design of unmanned electric flying wings. In particular, achieving adequate stability characteristics degrades the aerodynamic efficiency of the vehicle, and constrains the available volume in which subsystem components may be placed. Exploiting the potential advantages of electric flying wings therefore necessitates a multidisciplinary perspective. In order to overcome the identified challenges of unmanned electric flying wing design, a multidisciplinary design analysis framework was conceptualized, implemented, and evaluated. The Python-based framework synthesizes automated analysis modules that model geometry, weight distribution, electric propulsion, aerodynamics, stability, and performance. Virtual experiments demonstrated the framework’s utility in quickly exploring a wide design space and assessing design robustness. Two important stand-alone contributions developed for the framework are (1) an algorithm for densely packing battery cells within a wing shape and (2) a parametric electric propulsion analysis code. In short, the framework supports the design of small-scale (i.e. 0-55lb weight range) subsonic unmanned electric flying wings with a host of valuable capabilities that were previously unavailable within traditional design methods.
Decentralized allocation of safety-critical applications on parallel computing architecture

(Georgia Institute of Technology, 2019-08-26) Sutter, Louis

This work presents a decentralized task allocation algorithm for an abstract parallel computing architecture made of a set of Computational Units connected together, each of them being prone to fail. Such an architecture can represent for example a multi-core processor with each Computational Unit standing for one core. The aim of the algorithm is to find the best mapping between Computational Units and the different applications we want to execute on the architecture, while taking into account faulty Computational Resources and the priority of the applications. The proposed approach consists in formulating the allocation problem as an Integer Linear Program (ILP), that is solved thanks to a state-of-the-art ILP solver. The second main aspect of this work is the decentralization the allocation process, in the sense that no central element decides alone of the allocation for the rest of the network. Redundant copies of the allocation algorithm are executed on the architecture itself, meaning that the copies must reallocate themselves. Then, the proposed allocation process is implemented on an experimental setup reproducing the multi-core architecture that inspired this work. Each core is represented by a Raspberry Pi single board computer. The model is used to demonstrate the capabilities of the proposed allocation process to maintain operation of a physical system in a decentralized way, while individual components fail.
Investigation of ODE-based non-equilibrium wall shear stress models for large eddy simulation

(Georgia Institute of Technology, 2019-07-30) Dzanic, Tarik

For high Reynolds number flows, wall modeling is essential for performing large eddy simulation at a reasonable computational cost. In this work, a novel low-cost ODE-based non-equilibrium wall model is introduced for wall shear stress modeling in LES. Using polynomial approximations of the pressure gradient and convective terms obtained from interpolation of the LES solution, as opposed to direct evaluation of these gradients within the wall model, the governing wall model equations reduce from coupled PDEs to uncoupled ODEs that do not require an embedded wall model grid within the LES grid. Additionally, the steady form of the wall model equations was utilized, feasible due to the spatial decoupling of the wall model equations, and the effects of the temporal evolution on the wall shear stress were modeled. The effects of polynomial degree on the accuracy of the wall shear stress predictions were explored, and an empirical lag model was built to model the unsteady effects without requiring the solution of a time-stepping problem. Wall resolved large eddy simulations of separated flow around the NASA wall mounted hump and an iced NACA 63A213 airfoil were performed and used as a reference for the comparison of the non-equilibrium wall model to a commonly used equilibrium wall model. The proposed non-equilibrium wall model was able to predict separated flow and laminar flow regions in much better agreement with the wall resolved results than the equilibrium wall model. Underpredictions in the skin friction coefficient in non-equilibrium flow regimes were reduced from 20-50% to less than 10% between the equilibrium and the non-equilibrium wall modeled approaches. Minor improvements in the pressure coefficient predictions were observed with the non-equilibrium model in the separated flow region of the iced airfoil. The results suggest that the proposed wall model can offer better predictions of separated and/or laminar flows compared to equilibrium wall models with negligible computational cost increase.
Context dependent total energy alerting system for the detection of low energy unstabilized approaches

(Georgia Institute of Technology, 2019-07-05) Portman, Michael Aaron

This thesis examines context dependent total energy alerting to protect against low energy unstable approaches in commercial aviation operations. Currently, many individual states are monitored independently to identify unstable approaches, rather than an integrated single assessment of total energy. An alert would also have to be context dependent, integrating the individual states with awareness of phase of flight, approach profile modeling, and expected pilot response to individualize the alert’s activation threshold for each approach. This thesis details a design of such a context dependent total energy alerting system. First, a preliminary analysis examines when such an alert would have been given in a case study of Asiana Airlines Flight 214. This flight’s crash on approach into San Francisco International Airport was attributed to lack of pilot situational awareness and understanding of the aircraft’s autoflight systems, leading to the aircraft having sufficiently low total energy that it stalled into the seawall just before the runway threshold. Analysis shows the total energy alert would have sounded roughly 14-41 seconds before impact, earlier than any currently installed system and potentially early enough for corrective action. Next, the context dependent total energy alert is analyzed to assess its performance in real flight as captured by Flight Operations Quality Assurance (FOQA) data. The analysis examines how alerting parameters impact when and how often the alert is triggered, and the thesis concludes with recommendations for the design and application of a context dependent total energy alert, along with recommendations for future work.
A multi-UAV trajectory optimization methodology for complex enclosed environments

(Georgia Institute of Technology, 2019-05-02) Barlow, Sarah

Unmanned Aerial Systems (UAS) have become remarkably more popular over the past decade and demonstrate a continuous upward market trend. As UAS become more accessible and advanced, they are able to be incorporated into a broader range of applications and provide substantial operational benefits. In addition to exterior use cases, UAS are being investigated for interior use cases as well. An area that has great potential for UAV involvement are manufacturing and warehouse environments, as these typically occupy vast spaces. Warehouse logistics and operations are very complex and could significantly benefit from the integration of UAVs. Many companies are already exploring using UAS as a means to perform inventory audits to reduce labor costs and time, and improve accuracy and safety. To achieve the maximum benefit from this technology in these environments, multiple vehicles would be essential. The purpose of this thesis is to optimize the operations of multiple UAVs in complex and confined environments, using a warehouse model as a test case. There are added complexities when working with multiple vehicles; for example, ensuring that there are no collisions between vehicles. A great deal of research has been done on vehicle routing and trajectory optimization, but very little has been done with UAV optimization in confined spaces. This thesis further develops these algorithms and focuses in on the impact UAV involvement could have on operations in environments that are similar to warehouses. The proposed improvements from the current methods will help uncover the most optimal results by changing the process for finding solutions, the criteria under which solutions are ranked, and the operational/experimental setup. The new methodologies seek to resolve the sub-optimality issues from the existing approach to significantly reduce the mission time required to perform a warehouse inventory audit. An existing inventory scanning algorithm generates sub-optimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem and determining optimal deployment time without any collisions. To improve the sub-optimal results, this thesis introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These proposed improvements are assessed based on their degree of impact on the overall mission time compared to the current methods. They are also analyzed in comparison to one another and in combination with one another to better understand the effectiveness and sensitivities of the presented changes.
Hypersonic shape parameterization using class – shape transformation with stagnation point heat flux

(Georgia Institute of Technology, 2019-05-01) Fan, Justin

In recent years, hypersonics is undergoing a major resurgence that is primarily driven by domestic and foreign militaries to have an advanced and unchallenged weapon system. China and Russia have tested hypersonic systems, and the United States is pushing to match and exceed adversarial capabilities. While the concept of hypersonic vehicles is not a recently conceived concept, it has experienced turbulent progress throughout the decades. Hypersonic vehicles are inherently complex vehicles to design due to intricate couplings between design disciplines: aerodynamics, aerodynamic heating, trajectory, structures, and controls. As computational analysis tools in these disciplines have progressed, the geometries and vehicles must progress as well. For aerodynamic purposes, hypersonic vehicles often contain sharp leading-edges to achieve high lift-to-drag properties. However, the use of sharp leading edges at hypersonic velocities also results in severe aerodynamic heating. The severe aerodynamic heating can lead to the destruction of materials and the entire vehicle, as was the case in the Space Shuttle Challenger accident. The aerodynamic heating, specifically the stagnation point heat flux, has been found to be directly related to the leading-edge radius of a given shape. The purpose of this thesis is to implement the shape parameterization method known as the class-shape transformation (CST) method with stagnation point heat flux. The CST method is a proven method in research where geometries can be optimized in aerodynamics to obtain maximum lift-to-drag ratio (L/D). Instead of taking a shape and having to perform time-consuming analyses to determine the leading-edge heat flux, an initial geometry can be determined with approximate hypersonic operating conditions. The objective of this research is to 1) leverage a parametric shaping modeling method to generate geometries that 2) incorporates an aspect of hypersonic aerodynamic heating effects on the geometry and 3) optimize the new geometry for maximum aerodynamic efficiency.
A methodology for sequential low thrust trajectory optimization using prediction models derived from machine learning techniques

(Georgia Institute of Technology, 2019-04-30) Casey, John Alexander

Spacecraft trajectory sequence optimization has been a well-known problem for many years. Difficulty in finding adequate solutions arises from the combinatorial explosion of possible sequences to evaluate, as well as complexity of the underlying physics. Since there typically exists only minuscule amounts of acceptable solutions to the problem, a large search of the solution space must be conducted to find good sequences. Low thrust trajectories are of particular interest in this field due to the significant increase in efficiency that low thrust propulsion methods offer. Unfortunately, in the case of low thrust trajectory problems, calculations of the cost of these trajectories is computationally expensive, so estimates are used to restrict the search space before fully solving the trajectory during the mission planning process. However, these estimates, such as Lambert solvers, have been shown to be poor estimators of low thrust trajectories. Recent work has shown that machine learning regression techniques can be trained to accurately predict fuel consumption for low thrust trajectories between orbits. These prediction models provide an order of magnitude increase in accuracy over Lambert solvers while retaining a fast computational speed. In this work, a methodology is developed for integration of these machine learning techniques into a trajectory sequence optimization technique. First a set of training data composed of low thrust trajectories is produced using a Sims-Flanagan solver. Next, this data is used to train regression and classification models that respectively predict the final mass of a spacecraft after a low thrust transfer and predict the feasibility of a transfer. Two machine learning techniques were used: Gradient boosting and artificial neural networks. These predictors are then integrated into a sequence evaluation evaluation scheme that scores a sequence of targets to visit according to the prediction models. This serves as the objective function of the global optimizer. Finally, this objective function is integrated into a Genetic Algorithm that optimizes sequences of targets to visit. Since the objective function of this algorithm uses predictions to score sequences, the final sequence is evaluated by a Sims-Flanagan low thrust trajectory solver to evaluate the efficacy of the method. Additionally, a comparison is made between the global optimization results with two different objective functions: One based that score sequences using the machine learning predictors, and one that uses Lambert solvers to score sequences. This allows for a measurement of the this method's improvement in the global optimization results. Results of this work demonstrate that the developed methodology provides a significant improvement in the quality of sequences produced by the Genetic Algorithm when paired with the machine learning predictor based objective function. Both gradient boosting and artificial neural networks are shown to be accurate predictors of both the fuel usage and feasibility of low thrust trajectories between orbits. However, gradient boosting is found to offer improved results when evaluating sequences of targets to visit. When paired with the Genetic Algorithm global optimizer, both the gradient boosting prediction model and the artificial neural network model produce similar results. Both are shown to offer a significant improvement over the Lambert solver based objective function while maintaining similar speeds. The positive results this methodology yields lends support to the notion that the use of machine learning techniques has the potential to improve the optimization of sequences of low thrust trajectories. This work lays down a framework that can be applied to preliminary mission planning for space missions outfitted with low thrust propulsion methods. Such missions include, but are not limited to, multiple main-belt asteroid rendezvous, debris removal from Earth orbit, or an interplanetary tour of the solar system.
Ensuring pedestrian safety on campus through the use of computer vision

(Georgia Institute of Technology, 2019-04-26) Commun, Domitille Marie, France

In the United States alone, 5,987 pedestrians were killed and 70,000 injured in 2016 and 2015 respectively. Those numbers are of particular concern to universities where traffic accidents and incidents represent one of the main causes of injuries on campuses. On the Georgia Tech Campus, the growth of the population-to-infrastructure ratio, the emergence of new transportation systems, and the increase in the number of distractions have shown to have an impact on pedestrian safety. One means to ensure safety and fast responses to incidents on campus is through video surveillance. However, identifying risky situations for pedestrians from video cameras and feeds require significant human efforts. Computer vision and other image processing methods applied to videos may provide the means to reduce the cost and human errors associated with processing images. Computer vision in particular provides techniques that enable artificial systems to obtain information from images. While many vendors provide computer vision and image recognition capabilities, additional efforts and tools are needed to support 1) the mission of the Georgia Tech Police Department and 2) the identification of solutions or practices that would lead to improved pedestrian safety on campus. Data from cameras can be systematically and automatically analyzed to provide improved situational awareness and help to automate and better inform enforcement operations, identify conflict situations including pedestrians and provide calibration data to optimize traffic light control. In particular, this thesis aims at developing an intelligent system that automates data collection about incidents around campus and attempts to optimize traffic light control. This is achieved by: 1) Leveraging computer vision techniques such as object detection algorithms to identify and characterize conflict situations including pedestrians. Computer vision techniques were implemented to detect and track pedestrians and vehicles on surveillance videos. Once trajectories were extracted from videos, additional data such as speed, collisions and vehicle and pedestrian flows were determined. Such data can be used by the Georgia Tech Police Department to determine needs for agents to manage traffic at a given intersection. Speed information is used to detect speeding automatically, which can help to enforce law in an automated way. Traffic and walking light color detection algorithms were implemented and combined with location data to detect jaywalking and red light running. The conflict situations detected were stored in a database which completes the Police record database. The data is structured such as to enable statistics or the detection of patterns with improved processing time. Hence, the tool built in this thesis provides structured information about violations and dangerous situations around campus. This data can be used by the Police Department to automate law enforcement and issue citations automatically and to determine the needs for countermeasures to ensure pedestrian safety. 2) Implementing a simple optimized traffic light control system and setting up the inputs necessary for a an improved optimization of traffic light control using reinforcement learning. It is expected that the improved situational awareness and information gained from developing these capabilities will contribute to help reduce the number of collisions, the amount of dangerous jaywalking, and lead to new ways to ensure pedestrian safety on campus