Master of Science in Aerospace Engineering

Series

Master of Science in Aerospace Engineering

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https://hdl.handle.net/1853/73271

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Degree Series

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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Now showing 1 - 10 of 27

Conceptual Effectiveness-Based Hypersonic Evaluation (CEBREN)

(Georgia Institute of Technology, 2022-05-03) Van Der Linden, James C. A.

For decades, the United States has largely been uncontested in its quest to advance its national interests in every domain – to protect the American people, promote prosperity, preserve peace, and advance American influence. To maintain technological superiority, the National Security Strategy calls upon the military to field new capabilities that clearly overmatch US adversaries in lethality. Furthermore, the US military has identified hypersonics as an area of interest to stay competitive on the global stage. Hypersonics have been around for over 70 years ranging from the X-20 to the Space Shuttle; however, these projects were products of the traditional design-build-test methodology which often never saw flight. This design-build-test methodology is unable to meet the demands of technological growth and complexity and often drives up costs and overruns. Thus, there is a need to develop a new methodology for assessing hypersonic weapon capability rapidly to support interactive decision making for conceptual development. Hypersonic conceptual design distinguishes itself from traditional aircraft design because the disciplines that must be considered are highly coupled and tightly integrated which drastically increases design risk due to sources of uncertainty. Additionally, it is difficult for engineers to evaluate revolutionary designs because the historical data necessary to perform initial analysis likely is unavailable. Due to this uncertainty, conceptual design is critical because the decisions made have profound ramifications throughout the entire process. To address this uncertainty, physical experiments are required to provide the highest quality of data; however, they are extremely limited in scope and expensive. Hence, there is a need to make well informed decisions at the conceptual design level when designing novel hypersonic vehicles. Due to the coupling of disciplines within hypersonic conceptual design, a Multidisciplinary Design Analysis and Optimization (MDAO) environment was used to design novel hypersonic vehicles. To aid in evaluating these alternatives, agent-based modelling was used to study the effectiveness of the vehicles through operational analysis (OA). By integrating an MDAO environment with an OA framework, novel hypersonic vehicles were constructed, and their capabilities assessed through a process known as effectiveness-based design (EDB). Within EBD, the design objective is shifted from performance metrics (e.g., weight, range, etc.) to effectiveness metrics (e.g. targets killed, survival, etc.) which allows decision makers to consider and understand the implications of design-space-limiting decisions earlier in the process. This shifts away from over-defining requirements before exploring potential best solutions to the problem. Thus, the purpose of this thesis presents a new methodology to address the need of designing and rapidly assessing hypersonic capability to better inform the decision maker through the integration of OA within an MDAO environment thereby closing the loop by coupling the effectiveness to vehicle design parameters.
A Methodology to Capture the Acoustic Properties of Small Unmanned Aerial System Noise Using a Novel Frequency Weighting

(Georgia Institute of Technology, 2021-08-03) Gabrielian, Ana Bella

As the advent of Urban Air Mobility (UAM) draws near, the obstacles to such vehicles and operations grow larger. One of these obstacles is the noise created through the operation of these air vehicles. Noise is a public concern as excessive exposure has been shown to contribute to lack of sleep, lack of cognitive abilities in children, and decline in overall cardiac health. There is extensive noise policy for traditional aircraft; however, no noise policy exists for vehicles in the category of UAM. In this thesis, the understanding of small Unmanned Aerial System (sUAS) noise is detailed by investigating the competence of current metrics to describe the annoyance that is created by such vehicles. With regulatory entities such as the Federal Aviation Administration (FAA) forecasting the viability of last mile delivery by sUASs by 2030, it is imperative that acoustical understanding is developed in parallel with this emerging technology. As a part of a NASA research effort, the Design Environment for Novel Vertical Lift Vehicles (DELIVER), a psychoacoustic test on sUASs was conducted to measure human annoyance toward these vehicles in comparison to current delivery vehicles. The study had two main findings: at the same decibel level, test subjects found sUASs more annoying than they did delivery vehicles and the correlation between annoyance and decibel level using four different noise metrics was relatively low. In a preliminary comparison of spectral content between a helicopter and one of the sUASs in this study, it is shown that the sUAS’s spectral content has more tones in the region of frequencies in which humans are especially sensitive. To account for human sensitivity to these tones, the hypothesis is posed: A new frequency weighting, which allows Sound Exposure Level to better correlate with human annoyance caused by an sUAS noise event, will create a larger SEL contour area that is more indicative of sUAS noise. In the first phase of the approach, this hypothesis was tested by creating a design of experiments of different frequency weightings to find a new weighting with a higher correlation coefficient. The resulting frequency weighting (the X-weighting) increased the R2 value from 0.784 to 0.853. In the second phase, Sound Exposure Level contours were created using the new frequency weighting and current frequency weightings in ANOPP2. The SEL 65 dB contour experienced a 79%, 18%, and 78% increase in width, length and area respectively between then X- and the A-weighting for one of the sUASs investigated. This methodology grants stakeholders such as regulators and original equipment manufacturers a process to assess frequency weightings and their efficacy in capturing human annoyance; in doing so, this could enable all sUAS stakeholders to create a common “language” with which to discuss the noise created by these vehicles effectively.
A METHODOLOGY FOR CONDUCTING DESIGN TRADES FOR A SMALL SATELLITE LAUNCH VEHICLE WITH HYBRID ROCKET PROPULSION

(Georgia Institute of Technology, 2021-07-28) Caglar, Havva Irem

The commercial space industry has recently seen a paradigm shift related to the launch of a small satellite into Low Earth Orbit. In the past, a small satellite was launched as a secondary payload with a medium or heavy launch vehicle where the primary payload placed a constraint on the orbit and schedule. Today, a dedicated launch of a small launch vehicle is the main operational concept to launch a small payload. Many Smallsat Launch Vehicles (SLV) have been under development by the commercial space industry to improve these launch services in recent years. Despite these efforts, the specific prices per launch are still high, and reducing these prices further remains a challenge. One promising technology candidate to reduce costs for SLV is hybrid rocket propulsion which has matured recently with some cost and safety advantages. Although hybrid rocket propulsion faces a number of challenges, including a low regression rate and combustion instabilities, academia and commercial companies have invested significant resources in developing this technology. With this motivation, this thesis has focused on the conceptual design of SLV with hybrid rocket propulsion. Moreover, a cost reduction strategy currently used by the commercial space industry was observed to be the development of a unique engine and using multiple of them in a launch vehicle. Following this trend, the vehicle concept investigated in this thesis was an expendable ground-launched vehicle with some architectural variables such as the number of stages and the number of hybrid motors in each stage. The design trade-off studies of such a small multistage launch vehicle with multiple hybrid motors in each stage require very long times especially when traditional point design approaches are used. As the number of design variables increase, the design space exploration becomes even more challenging. To provide a solution to this problem, a methodology for rapid conceptual design of such a vehicle was presented in this thesis. A physics-based conceptual design approach was followed in this study since SLV are relatively new concepts without much historical performance data. To conduct a multidisciplinary analysis, a physics-based, integrated modeling and simulation environment was constructed with four core disciplines: trajectory analysis, aerodynamics, propulsion, and weight. Aerodynamics and propulsion analysis were conducted using a first-principles approach, which was based on fundamental theories. A 3 Degree of Freedom (DOF) industrial, transparent, physics-based trajectory analysis software was used in this study based on availability. However, any other trajectory analysis software that a system designer is familiar with can be used in its place. In other words, the methodology developed in this thesis would remain unchanged if another trajectory analysis software were used. The weight discipline was represented at a high level by using Propellant Mass Fraction (PMF) design variable. A multidisciplinary modeling and simulation environment for launch vehicles may be computationally expensive depending on the fidelity levels of each discipline. Moreover, trajectory optimization is included in a launch vehicle design process conventionally which may be also computationally expensive depending on the optimization method. This expense poses a difficulty in performing a trade-off study for hundreds of vehicle design alternatives within the constraints of the schedule in the conceptual design phase. Because of this, trajectory optimization was removed from the design process to speed up the process by selecting a constant controller design. The methodology developed in this thesis consisted of two sequential steps. In the first step, a surrogate modeling approach was followed to replace the Modeling and Simulation (M&S) environment. A DOE method and a surrogate modeling method suitable to this problem were searched in this part. To cover the design space, a hybrid DOE consisting of a Fast Flexible Filling DOE and a three-level Full Factorial DOE was chosen. Artificial Neural Networks method was selected to fit approximation models because of the type of design variables (both continuous and discrete variables) and nonlinearity of the problem. The first experiment was conducted to test this hypothesis. As a result, it was demonstrated that this approach can provide accurate surrogate models for any desired response. In the second step, the specific mechanical energy-based design trade-off method was developed using some statistical methods. This method estimates the lower bound of the vehicles’ actual specific mechanical energy where the vehicles can be rapidly designed by using surrogate models. This lower bound was predicted with the help of the prediction interval of the specific mechanical energy’s model fit error. To fit the surrogate models, the necessary data were gathered by running the DOE in the integrated M&S environment while imposing some terminal conditions on the altitude of the vehicles analyzed in this environment. Specifically, the surrogate models of specific mechanical energy and flight path angle were used to design the vehicles rapidly. The second experiment was conducted to test this hypothesis. As a result, the actual specific mechanical energies computed via trajectory optimization were found to be consistent with the predictions. Overall, it was demonstrated that the proposed method enables a system designer to rapidly design some feasible vehicles, which can then proceed to the next design phase for further comparison, analysis, and design.
A Methodology for Demand Assessment and Integrated Schedule Design and Fleet Assignment Applied to Thin-Haul Scheduled Operations

(Georgia Institute of Technology, 2021-07-07) Da Silva Oliveira, Thayna

The thin-haul market is characterized by short-range routes with low demand, occasionally served by commuter airlines. Historically, commuter operators have not been able to maintain profitable operations in this market, migrating to longer and more profitable routes throughout the years. As a result, many small cities have lost their air service and airports have become underutilized. Aiming to change this scenario, many studies have focused on the development of vehicle technologies to promote thin-haul scheduled operations and the assessment of potential demand. This thesis investigates thin-haul operations from the airline's point of view, aiming to understand how flight operations optimization can aid commuter operators to improve profitability and, ultimately, to restore the air service to small communities. Despite the low individual demand of each thin-haul route, an opportunity for profitability may exist if the origin-destination pairs are effectively served. This can be achieved if the airline makes the right schedule decisions, i.e., strategically defines when and where to fly, as well as the assignment of the aircraft with the right capacity to the right flight leg. These problems are part of the schedule planning process and are known in the literature as schedule design and fleet assignment (SD&FA). However, the lack of historical data and baseline schedule for thin-haul operations imposes challenges for demand estimation and SD&FA applications. Therefore, the contribution of this thesis is in the development of a methodology for demand assessment and integrated SD&FA applied to thin-haul operations that can overcome the aforementioned challenges. This is achieved by investigating thin-haul demand based on the competition with alternative modes of transport and by coupling the current SD&FA techniques with the concept of hourly demand distribution. The proposed methodology is implemented in a framework that allows different operational scenarios to be evaluated based on the operations metrics of effectiveness, which includes the airline profit, the potential thin-haul demand served, and the passenger time savings. Such framework enables stakeholders to understand the key elements that lead to profitable thin-haul operations, the extent to which the air service can be expanded, and the potential benefits for passengers and cities. The experiments conducted in this thesis demonstrated that the methodology can successfully perform SD&FA applied to thin-haul operations and determine the true market share, i.e., the potential demand that can be profitably served by an air carrier. Additional case studies highlighted that more efficient operations can be achieved if airlines adopt a mix of point-to-point and connecting flights, and that hub location and aircraft attributes can significantly impact the effectiveness of the operations.
An acoustical based approach to conceptual design of non-traditional rotorcraft configurations

(Georgia Institute of Technology, 2020-07-31) Huelsman, Sara

As interstate and highway traffic increases, commute times become drastically large. Such large commute times create fatigue and take away from productive hours at work, or joyful hours at home. The idea of urban air mobility becomes increasingly more attractive and viable as technology improves. These more advanced rotor concepts have opened up the design space in order to satisfy a very different mission profile. Nontraditional rotor concepts can provide performance benefits within a new use of the design. Noise becomes an increasing concern since the mission profile allows these vehicles flying much closer to communities. This research investigates three configurations of rotorcraft: coaxial rotors, ducted rotors, and ducted coaxial rotors, to provide insight on how design configuration changes the acoustics of these vehicles. The methodology developed is a parametric environment to provide detail on influential parameters for a model to be created for use within the conceptual design stage. This provides designers a process for capturing acoustic changes early on in the design process, while these new vehicles are still being developed.
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.