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Master of Science in Aerospace Engineering
Master of Science in Aerospace Engineering
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ItemAssessing An Aerospace Application Of Digital Twins For Multi-Agent Dynamic Decision Making(Georgia Institute of Technology, 2023-05-02) Marks, IanThe concept of Dynamic Decision Making (DDM) is essential for achieving an overall goal by adapting to the results of previous decisions and unexpected environmental changes. Example applications of DDM in aerospace vary from individual predictive maintenance to multi agent tasking . When making dynamic decisions in a multi-agent scenario, the goal is to minimize uncertainty for future actions by predicting consequences for both the individual asset and the group. In a squadron with vehicles of the same type, it is expected that performance (e.g., fatigue rate and structural health ) vary form one vehicle to the next. Infusing individual performance capabilities and their uncertainties can overwhelm the decision maker. One approach to improve the decision-making process for multiple agents is by using Digital Twins, an authoritative virtual representation of a connected physical system. The digital twin’s aspects of computational, physical, and communications limits impact their overall utility. Furthermore, the aspects of fidelity, runtime, latency, and proximity (due to the physical requirements) need to be assessed to determine the value within multi-agent DDM. A vision for Digital Twins is to enable real time operational decision making by predictive and proactive measures while mitigating potential anomalies. This thesis seeks to evaluate the infusion of Digital Twins in a multi agent DDM architecture, the challenges with the infusion, and a comparison to historically deterministic decision-making processes for a relevant aerospace scenario to trade overall mission effectiveness. To that end, three steps are required: a method of evaluating different decision-making architectures, digital twin selection, and scenario definition. A structured decision-making process was developed such that both twinned and twinless multi agent DDM methods could be interchanged. The digital twin selected for evaluation was the airframe prognostic health of a remote-control aircraft. The digital twin determined how tightly a turn can be performed ( or ) as a function of health status mid-mission. A field surveillance/survey mission scenario was implemented with area surveilled as a metric. During the mission, each aircraft (twinned or twinless) defines their turn load, while a multi-agent coordinator modifies waypoints for agents. To ensure multi-agent interactions with DDM, a perturbance (treated as a gust event) occurs leading to one aircraft leaving the mission early and requiring the remaining aircraft to adapt their missions to mitigate the unexplored areas. Each aircraft leaves the mission area upon mission completion, digital twin health assessments or crashing. The assessment for permitting aircraft to leave the mission area is traded between the multi agent commander and by agents; both traded as a function of latency. Each agent has unique variations in both airframe life and digital twin architectures (instance vs aggregate) and are traded. The design of experiments enables trades across the agents factors of the digital twin fidelity (fit error with sensor to loads), initial health, and overall system latency. From the data generated, surrogate models were fit and analyzed to determine variable significance via ANOVA as well as a comparison between a turn only (treated as a twinless/human baseline) and various digital twin fidelities. Sensitivity analysis revealed that airframe life had the greatest impact on overall mission effectiveness among both digital twin-infused dynamic decision-making methods. Following closely was the influence of overall system latency, with digital twin fidelity being least important of the three. Additionally, the digital twin comparisons to human baseline show that digital twins significantly increase mission performance by longevity in the field as the entire fleet significantly ages. A simplified axiom for the digital twin’s infusion into multi agent dynamic decision making is as follows: 1) Having information is good (digital twin usage) 2) Having accurate information is better (digital twin fidelity) 3) Having information on time to make decisions is critical (data communication)
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ItemA systems of systems methodology for conceptual studies of in-situ resource utilization for near earth object applications(Georgia Institute of Technology, 2020-07-28) Kitson, Christopher CurticeNear Earth Objects (NEO) have historically been neglected as an object of study relative to other celestial bodies. Interest has been increasing as more recognize the potential value of NEO resources represented by ‘asteroid mining’, especially as a supporting role in a Systems of Systems (SoS) context. After all, reusable rockets require refueling before reuse. That propellant needs to come from somewhere. Still, a feasible means to harness NEO resources has proven elusive. In-Situ Resource Utilization (ISRU) is a broad field with literature siloed by both disciplines and use cases. This is especially apparent for existing NEO ISRU concepts, with wildly varying levels of detail between systems in the same concept, including omission of key functions. Pet projects given context imply ‘technology push’ instead of ‘mission pull’. This thesis aims to show NEO ISRU is more feasible than previously believed, by providing a more comprehensive treatment of the required functionality and the means to deliver it. This boils down to permitting better comparisons via enabling trade studies at the conceptual level (NASA pre-phase A). A sample return mission using propellant produced from NEO resources for the return trip is formulated to contextualize the analysis. A program to develop a design that accomplishes this mission could be named “Sample return from Near earth object with In-situ Propellant production Technology demonstrator” (SNIPT). Both qualitative and quantitative design aspects are considered herein. Qualitative aspects are considered first. By reconciling commonalities between concepts, standardized terminology is proposed through a functional decomposition along with a morphological matrix of alternatives. A streamlined technology readiness assessment is performed to rank these morphological options. This information is used to select four concepts, one for each propellant type considered. Both impulsive (methalox and hydrolox) and continuous (hydrogen and steam) propulsion are considered as possible customers of an In-Situ Propellant Production (ISPP) SoS. Another significant part of this effort is quantifying alternatives sufficiently to permit comparisons beyond subject matter expert opinions. A modular sizing code is developed from scratch in line with the selected morphological options for each propellant, and verified at the module level using analog test data. By establishing baseline design(s), perturbations can be compared with directionally correct results. Input parameters for NEO orbital characteristics and then NEO composition are varied to ascertain effects upon sizing results. These results inform a trade study between the four propellant types considered. It was found that previous modeling efforts for NEO ISRU concepts have grossly underestimated the overall plant mass, likely due to neglecting indirect ISRU functionality and energy use. This includes sized values for mass payback ratio (MPR ≈ 5) and mass-specific regolith throughput (f_REG ≈ 0.3 day^(-1) ) which were previously overestimated by orders of magnitude. Methalox works better above 5 C: 1 H atoms by mass, a restrictive niche. Steam had the highest MPR but also heaviest plant mass. Hydrolox was found to be lightest on average for low Δv, with hydrogen lighter for high values, though hydrogen had MPR < 1 due to low volatile utilization. Increasing the proportion of volatiles used to make the propellant was found to reduce specific energy intensity, which in turn increases MPR.
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ItemA methodology to reduce dimensionality of a commercial supersonic transport design space using active subspaces(Georgia Institute of Technology, 2020-04-28) Crane, Nathan ThomasAs the commercial aviation industry continues to grow, the next technological leap is speed, and commercial supersonic transports are reappearing from multiple companies. Although this problem has been solved before, supersonic design is still difficult as it is highly interdisciplinary, lacks historical data, and requires additional design considerations earlier in the design cycle. Without historical data, higher fidelity analysis is needed early in the design process. The large number of design variables and the need for high fidelity analysis creates large computational costs, limiting design space exploration. To address this, the dimensionality of the design space needs to be reduced without removing the effects from the design variables. A recent technique called Active Subspaces has accomplished this goal by rotating a design space into the most active direction and taking surrogates in this active direction. Through rotation, the effects of each design variable are still present, but less impactful directions can be removed from the surrogate model, reducing dimensionality. This research applies this method to a commercial supersonic design space and asks additional questions about active subspace implementation into a design methodology. These questions address the gradient oversampling needed for good active subspace surrogate fits, if a better active subspace could be found in a partition of the full design space, and how the goodness of an initial surrogate, used to calculate gradients, affects the active subspace surrogate. Finally, the research compares computational cost between a traditional surrogate and an active subspace surrogate. These questions were addressed using aerodynamic data of various aircraft configurations at supersonic cruise conditions. Beginning with a design of experiments of 20 planform variables, the configurations were input into Engineering Sketch Pad to generate the geometry. The geometry was taken into an inviscid computational fluid dynamics (CFD) tool to calculate coefficients of lift and drag at the cruise condition, and these were tabulated. The results were post processed, and a traditional surrogate was created. From this surrogate, gradients were taken to develop active subspace variables. These variables were used to generate a sweep of active subspace surrogates starting from a single variable to a surrogate made from all 20 variables. From these surrogates, it was concluded that oversampling gradients beyond the published range does not decrease error while undersampling increases error at a lower significance than expected. An active subspace in a local partition of a design space initially reduced error, but error reduction decreased as more variables were included in the active subspace surrogate. The number of cases per design variable of an initial surrogate used to calculate gradients was significant. The error of the active subspace surrogate created from these gradients decreased until 50 cases per design variable, when the decrease in error plateaued. Finally, active subspaces saw a large potential to reduce computational time. A small reduction in dimensionality could greatly reduce computational time, especially if gradients are found within a tool. Using these results, a design methodology was presented incorporating active subspaces into the design loop.
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ItemDevelopment of a methodology for technology requirement assessment for space habitats(Georgia Institute of Technology, 2020-04-28) Deguignet, MarieThere recently has been a renewed focus on space exploration and space habitats all over the world. Future lunar developments should focus on reusability, sustainability and affordability. To comply with these objectives, deep space exploration will be faced with technical and human limitations. New technologies must be developed to overcome these challenges. Because technology development is a long and onerous process, it is important to be able to identify the requirements early in the design process to reduce the risk of new developments. A clear methodology to evaluate the requirements of a technology to meet future goals must be provided to innovative companies. This work aims at establishing a clear and consistent methodology to evaluate future space technologies and compare their impact on several factors of a campaign to define the conceptual requirements. To prove that the developed methodology answers all the targeted requirements of the research objective, it will be tested on a technology: cryocoolers, and the space logistics framework FOLLOW. The proposed methodology uses Technology Impact Forecasting and applies and modifies it to take into consideration the specificity of the problem at hand: a smaller data set, long computation times and the goal of the thesis. The methodology can be used by companies to prove the worth of new innovative ideas and encourage investment. It is a rather safe process to help technology advancement.
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ItemMulti-mission sizing and selection methodology for space habitat subsystems(Georgia Institute of Technology, 2019-12-11) Boutaud, Agathe KathiaFuture 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.
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ItemDevelopment of a Multidisciplinary Design Analysis Framework for Unmanned Electric Flying Wings(Georgia Institute of Technology, 2019-12-03) Whitmore, William ValentinSmall-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.
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ItemA multi-UAV trajectory optimization methodology for complex enclosed environments(Georgia Institute of Technology, 2019-05-02) Barlow, SarahUnmanned 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.
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ItemA methodology for conducting design trades related to advanced in-space assembly(Georgia Institute of Technology, 2018-12-07) Jara de Carvalho Vale de Almeida, LourencoIn the decades since the end of the Apollo program, manned space missions have been confined to Low Earth Orbit. Today, ambitious efforts are underway to return astronauts to the surface of the Moon, and eventually reach Mars. Technical challenges and dangers to crew health and well-being will require innovative solutions. The use of In-Space Assembly (ISA) can provide critical new capabilities, by freeing designs from the size limitations of launch vehicles. ISA can be performed using different strategies. The current state-of-the-art strategy is to dock large modules together. Future technologies, such as welding in space, will unlock more advanced strategies. Advanced assembly strategies deliver smaller component pieces to orbit in highly efficient packaging but require lengthy assembly tasks to be performed in space. The choice of assembly strategy impacts the cost and duration of the entire mission. As a rule, simpler strategies require more deliveries, increasing costs, while advanced strategies require more assembly tasks, increasing time. The effects of these design choices must be modeled in order to conduct design trades. A methodology to conduct these design trades is presented. It uses a model of the logistics involved in assembling a space system, including deliveries and assembly tasks. The model employs a network formulation, where the pieces of a structure must flow from their initial state to a final assembly state, via arcs representing deliveries and assembly tasks. By comparing solutions obtained under different scenarios, additional design trades can be performed. This methodology is applied to the case of an Artificial Gravity Space Station. Results for the assembly of this system are obtained for a baseline scenario and compared with results after varying parameters such as the delivery and storage capacity. The comparison reveals the sensitivities of the assembly process to each parameter and the benefits that can be gained from certain improvements, demonstrating the effectiveness of the methodology.
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ItemA nonparametric-based approach on the propagation of imprecise probabilities due to small datasets(Georgia Institute of Technology, 2018-04-25) Gao, ZhenyuQuantification of uncertainty (UQ) is typically done by the use of precise probabilities, which requires a very high level of precision and consistency of information for the uncertain sources, and is rarely available for actual engineering applications. For better accuracy in the UQ process, greater flexibility in accommodating distributions for uncertain sources is needed to base inferences on weaker assumptions and avoid introducing unwarranted information. Latest literatures proposed a parametric-based approach for the propagation of uncertainty created by lack of sufficient statistical data, yet still has some notable limitations and constraints. This work proposes a nonparametric-based approach that facilitates the propagation of uncertainty in the small dataset case. The first part of this work uses Kernel Density Estimation (KDE) and Bootstrap to estimate the probability density function of a random variable based on small datasets. As a result, two types of sampling densities for propagating uncertainty are generated: an optimal sampling density representing the best estimate of the true density, and a maximum variance density representing risk and uncertainty that is inherent in small datasets. The second part extends the first part, to generate two-dimensional nonparametric density estimates and capture dependencies among variables. After a process to confirm the correlation among the variables based on small datasets, Copulas and the Sklar's Theorem are used to link the marginal nonparametric densities and create joint densities. By propagating the joint densities for dependent variables, researchers can prevent uncertainty in the outputs from being underestimated or overestimated. The effectiveness of the nonparametric density estimation methods is tested by selected test cases with different statistical characteristics. A complete uncertainty propagation test through a complex systems model is also conducted. Finally, the nonparametric-based methods developed in this thesis are applied to a challenging problem in aviation environmental impact analysis.
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ItemA physics based robust methodology for aerodynamic design analysis and optimization(Georgia Institute of Technology, 2000-08) Jimeno, Jesus