Organizational Unit:

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 26

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.
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.
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
Machine learning regression for estimating characteristics of low-thrust transfers

(Georgia Institute of Technology, 2019-04-26) Chen, Gene Lamar

In this thesis, a methodology for training machine learning algorithms to predict the fuel and time costs of low-thrust trajectories between two objects is developed. In order to demonstrate the methodology, experiments and hypotheses were devised. The first experiment identified that a direct method was more efficient than an indirect method for solving low-thrust trajectories. The second experiment, an offshoot of the first, found that the Sims-Flanagan method as implemented in the Python library PyKEP would be the most efficient manner of creating the training data. The training data consisted of the orbital elements of both the departure and arrival bodies, as well as the fuel and time-of-flight associated with a transfer between those bodies. A total of 7,218 transfers made up the training data. After creating the training data, the third and final experiment could be conducted, to see if machine learning methods could accurately predict fuel and time costs of low-thrust trajectory for a larger design space that had been investigated in previous literature. As such, the training data consisted of transfers, generated using a space-filling Latin Hypercube design of experiments, between bodies of highly varying orbital elements. The departure and arrival bodies’ semimajor axis and inclination differ much more than in previous literature. It was found that all the machine learning regression methods analyzed greatly outperformed the Lambert predictor, a predictor based on the impulsive thrust assumption. The accuracy of the time-of-flight prediction was close to that of the mass prediction when considering the mean absolute error of the expended propellant mass.
System identification of a general aviation aircraft using a personal electronic device

(Georgia Institute of Technology, 2019-04-25) Nothem, Michael

System Identification (SysID) is the process of obtaining a model of system dynamics by analyzing measurement data. SysID is often used in flight testing to obtain or refine estimates for aircraft stability and control derivatives and performance. Recent applications have shown that SysID can also be used to monitor and update models of dynamics and performance during routine operations. General Aviation (GA) continues to see higher accident rates than other aviation sectors. To combat this, research into accident mitigation strategies, especially loss of control (LOC) accidents, has led to the development of energy-based or envelope-based safety metrics that can be used to monitor and improve the safety and efficiency of GA operations. However, these methods depend on the existence of an accurate aircraft model to predict the performance and dynamics of the aircraft. The diversity of the aging GA fleet has established the need to calibrate existing models using flight data. SysID therefore has the potential to improve these methods by monitoring and updating aircraft models for each individual GA aircraft. Any SysID process depends on the type and quality of measurement data available as well as the nature of the aircraft model (what parameters are being identified) and the method of SysID being used. As opposed to flight test SysID, availability of flight data can be limited in GA. However, flight data recording using Personal Electronic Devices (PEDs) or low-cost Flight Data Recorders (FDRs) is becoming common. The capabilities of SysID methods using data from these devices has yet to be explored. This work demonstrates a process for evaluating SysID techniques for GA aircraft using data from a PED. A simulator environment was created that allowed testing of a variety of SysID and estimation methods. An observability condition was developed and used to inform decisions regarding model parameters and necessary assumptions. The results of this process provide a proof for existence and uniqueness of a solution to the minimization problem that SysID aims to solve. Local observability and global identifiability were also used to divide the “blind” SysID process into two estimations: an online estimation of aircraft states and unknown controls, and an offline identification of model parameters. Two SysID methods were then compared: Output Error Method (OEM), and Filter Method using an Extended Kalman Filter (EKF). It was shown that OEM outperformed EKF at the expense of increased computational burden. Potential improvements to both OEM and EKF SysID in this context are discussed. However, using OEM resulted in improved estimates of performance and dynamics over an assumed a priori model. These improvements were robust to both sensor quality and assumptions in the model, therefore demonstrating the potential of SysID using PED data to improve GA safety and efficiency.
Predicting the occurrence of ground delay programs and their impact on airport and flight operations

(Georgia Institute of Technology, 2019-04-25) Mangortey, Eugene

A flight is delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common delays and can be caused by the initiation of Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP). A Ground Delay Program is implemented to control air traffic volume to an airport over a lengthy period when traffic demand is projected to exceed the airport's acceptance rate due to conditions such as inclement weather, volume constraints, closed runways or equipment failures. Ground Delay Programs cause flight delays which affect airlines, passengers, and airport operations. Consequently, various efforts have been made to reduce the impacts of Ground Delay Programs by predicting their occurrence or the optimal time for initiating Ground Delay Programs. However, a few research gaps exist. First, most of the previous efforts have focused on only weather-related Ground Delay Programs, ignoring other causes such as volume constraints and runway-related incidents. Second, there has been limited benchmarking of Machine Learning techniques to predict the occurrence of Ground Delay Programs. Finally, little to no work has been conducted to predict the impact of Ground Delay Programs on flight and airport operations such as their duration, flight delay times, and taxi-in time delays. This research addresses these gaps by 1) fusing data from a variety of datasets (Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS)) and 2) leveraging and benchmarking Machine Learning techniques to develop prediction models aimed at reducing the impacts of Ground Delay Programs on flight and airport operations. These models predict 1) flight delay times due to a Ground Delay Program, 2) the duration of a Ground Delay Program, 3) the impact of a Ground Delay Program on taxi-in time delays, and 4) the occurrence of Ground Delay Programs. Evaluation metrics such as Mean Absolute Error, Root mean Squared Error, Correlation, and R-square revealed that Random Forests was the optimal Machine Learning technique for predicting flight delay times due to Ground Delay Programs, the duration of Ground Delay Programs, and taxi-in time delays during a Ground Delay Program. On the other hand, the Kappa Statistic revealed that Boosting Ensemble was the optimal Machine learning technique for predicting the occurrence of Ground Delay Programs. The aforementioned prediction models may help airlines, passengers, and air traffic controllers to make more informed decisions which may lead to a reduction in Ground Delay Program related-delays and their impacts on airport and flight operations.
Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes

(Georgia Institute of Technology, 2019-04-24) Dard, Ghislain

One of the missions of the Federal Aviation Administration (FAA) is to maintain the safety and efficiency of the National Airspace System (NAS). One way to do so is through Traffic Management Initiatives (TMIs). TMIs, such as reroute advisories, are issued by Air Traffic Controllers whenever there is a need to balance demand with capacity in the National Airspace System. Indeed, rerouting flights ensures that aircraft comply with the air traffic flow, remain away from special use airspace, and avoid saturated areas of the airspace and areas of inclement weather. Reroute advisories are defined by their level of urgency i.e. Required, Recommended or For Your Information (FYI). While pilots almost always comply with required reroutes, their decisions to follow recommended reroutes vary. Understanding the efficiency and relevance of recommended reroutes is key to the identification and definition of future reroute options. Similarly, being able to predict the issuance of volume-related reroute advisories would be of value to airlines and Air Traffic Controller (ATC). Consequently, the objective of this work was two-fold: 1) Assess the relevancy of existing recommended reroutes, and 2) predict the issuance and the type of volume-related reroute advisories. The first objective has been fulfilled by fusing relevant datasets and developing flights compliance metrics and algorithms to assess the compliance of flights to recommended reroutes. The second objective has been fulfilled by fusing traffic data and reroute advisories and then benchmarking Machine Learning techniques to identify the one that performed the best.