Organizational Unit:

Daniel Guggenheim School of Aerospace Engineering

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

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Organizational Unit

College of Engineering

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Organizational Unit

Aerospace Design Group

Organizational Unit

Aerospace Systems Design Laboratory (ASDL)

Organizational Unit

Air Transportation Laboratory

Organizational Unit

Cognitive Engineering Center

Organizational Unit

Space Systems Design Laboratory (SSDL)

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ArchiveSpace Name Record

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

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Publication Search Results

Now showing 1 - 10 of 2637

Using sample-based continuation techniques to efficiently compute subspace reachable sets and Pareto surfaces

(Georgia Institute of Technology, 2019-11-11) Brew, Julian ; Lightsey, E. Glenn ; Holzinger, Marcus J. ; Schuet, Stefan ; Tsiotras, Panagiotis ; Rogers, Jonathan ; Aerospace Engineering

For a given continuous-time dynamical system with control input constraints and prescribed state boundary conditions, one can compute the reachable set at a specified time horizon. Forward reachable sets contain all states that can be reached using a feasible control policy at the specified time horizon. Alternatively, backwards reachable sets contain all initial states that can reach the prescribed state boundary condition using a feasible control policy at the specified time horizon. The computation of reachable sets has been applied to many problems such as vehicle collision avoidance, operational safety planning, system capability demonstration, and even economic modeling and weather forecasting. However, computing reachable volumes for general nonlinear systems is very difficult to do both accurately and efficiently. The first contribution of this thesis investigates computational techniques for alleviating the curse of dimensionality by computing reachable sets on subspaces of the full state dimension and computing point solutions for the reachable set boundary. To compute these point solutions, optimal control problems are reduced to initial value problems using continuation methods and then solved. The sample-based continuation techniques are computationally efficient in that they are easily parallelizable. However, the distribution of samples on the reachable set boundary is not directly controlled. The second contribution presents necessary conditions for distributed computation convergence, as well as necessary conditions for curvature- or uniform coverage-based sampling methods. Solutions to multi-objective optimization problems are generally defined using a set of feasible solutions such that for any one objective to improve it is necessary for other objectives to degrade. This suggests there is a connection between the two fields with the potential of cross-fertilization of computational techniques and theory. The third contribution explores analytical connections between reachability theory and multi-objective optimization with investigation into properties, constraints, and special cases.
Methodology for global optimization of computationally expensive design problems

(Georgia Institute of Technology, 2013-06-25) Koullias, Stefanos ; Mavris, Dimitri N. ; Griendling, Kelly ; Mahadevan, Sankaran ; Schrage, Daniel P. ; German, Brian J. ; Aerospace Engineering

The design of unconventional aircraft requires early use of high-fidelity physics-based tools to search the unfamiliar design space for optimum designs. Current methods for incorporating high-fidelity tools into early design phases for the purpose of reducing uncertainty are inadequate due to the severely restricted budgets that are common in early design as well as the unfamiliar design space of advanced aircraft. This motivates the need for a robust and efficient global optimization algorithm. This research presents a novel surrogate model-based global optimization algorithm to efficiently search challenging design spaces for optimum designs. The algorithm searches the design space by constructing a fully Bayesian Gaussian process model through a set of observations and then using the model to make new observations in promising areas where the global minimum is likely to occur. The algorithm is incorporated into a methodology that reduces failed cases, infeasible designs, and provides large reductions in the objective function values of design problems. Results on four sets of algebraic test problems are presented and the methodology is applied to an airfoil section design problem and a conceptual aircraft design problem. The method is shown to solve more nonlinearly constrained algebraic test problems than state-of-the-art algorithms and obtains the largest reduction in the takeoff gross weight of a notional 70-passenger regional jet versus competing design methods.
Numerical solutions to optimal low- and medium-thrust orbit transfers

(Georgia Institute of Technology, 1993-08) Goodson, Troy D. ; Chuang, Chien-Hsiung ; Aeronautical Engineering
Field observations and data analysis of upper atmosphere and luminescence

(Georgia Institute of Technology, 1971) Edwards, Howard Dawson ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. School of Aerospace Engineering ; Georgia Institute of Technology. Office of Sponsored Programs
Liner impedance modification by varying perforate orifice geometry

(Georgia Institute of Technology, 1998-12) Gaeta, Richard Joseph, Jr. ; Ahuja, Krishan K. ; Aeronautical Engineering
A theoretical and experimental analysis of lengthwise pressure gradient for flow of air in small bore tubing considering the effect of elevated temperature

(Georgia Institute of Technology, 1957-08) Laster, Marion Lynn ; Ducoffe, Arnold L. ; Aeronautical Engineering
A risk-value-based methodology for enterprise-level decision-making

(Georgia Institute of Technology, 2017-07-31) Burgaud, Frederic ; Mavris, Dimitri N. ; Schrage, Daniel P. ; Garcia, Elena ; Clarke, Jonathan E. ; Goldsman, David ; Aerospace Engineering

Despite its long lasting existence, aerospace remains a non-commoditized field. To sustain their market domination, the major companies need to commit to large capital investments and constant innovation, in spite of multiple sources of risk and uncertainty, and significant chances of failure. This makes aerospace programs particularly risky. However, successful programs more than compensate the costs of disappointing ones. In order to maximize the chances of a favorable outcome, a business-driven, multi-objective, and multi-risk approach is needed to ensure success, with particular attention to financial aspects. Additionally, aerospace programs involve multiple divisions within a company. Besides vehicle design, finance, sales, and production are crucial disciplines with decision power and influence on the outcome of the program. They are also tightly coupled, and the interdependencies existing between these disciplines should be exploited to unlock as much program-level value potential as possible. An enterprise-level approach should, therefore, be used. Finally, suborbital tourism programs are well suited as a case study for this research. Indeed, they are usually small companies starting their projects from scratch. Using a full enterprise-level analysis is thus necessary, but also more easily feasible than for larger groups. These motivations lead to the formulation of the research objective: to establish a methodology that enables informed enterprise-level decision-making under uncertainty and provides higher-value compromise solutions. The research objective can be decomposed into two main directions of study. First, current approaches are usually limited to the design aspect of the program and do not provide the optimization of other disciplines. This ultimately results in a de-facto sequential optimization, where principal-agent problems arise. Instead, a holistic implementation is proposed, which will enable an integrated enterprise-level optimization. The second part of this problem deals with decision-making with multiple objectives and multiple risks. Current methods of design under uncertainty are insufficient for this problem. First, they do not provide compelling results when several metrics are targeted. Additionally, variance does not properly fit the definition of risk, as it captures both the upside and downside uncertainty. Instead, the deviation of the Conditional Value at Risk (called here downside deviation) is used as a measure of value risk. Furthermore, objectives are categorized and aggregated into risk and value scores to facilitate convergence, visualization, and decisionmaking. As suborbital vehicles are complex non-linear systems, with many infeasible concepts and computationally expensive M&S environments, a time-efficient way to estimate the downside deviation needs to be used. As such, a new uncertainty propagation structure is used that involves regression and classification neural networks, as well as a Second-Order Third-Moment (SOTM) technique to compute statistical moments. The proposed process elements are combined, and integrated into a method following a modified Integrated Product and Process Development (IPPD) approach, using five main steps: establishing value, generating alternatives, evaluating alternatives, and making decisions. A new M&S environment is implemented and involves a design framework to which several business disciplines are added. A bottom-up approach is used to study the four research questions of this dissertation. At the lowest level of the implementation, an enhanced financial analysis is evaluated. Common financial valuation methods used in aerospace have heavy limitations: all of them rely on a very arbitrary discount rate despite its critical impact on the final value of the NPV. The proposed method provides detailed analysis capabilities and helps capture more value by enabling the optimization of the company’s capital structure. A sensitivity analysis also verifies the importance of the added factors in the proposed method. The second implementation step is to time-efficiently evaluate downside deviation. As such, regression and classification neural networks are implemented to estimate the base costs of the vehicle and speed up the vehicle sizing process. Business analyses are already time-efficient and therefore maintained. These neural networks ultimately show good validation prediction root-mean-square error (RMSE), which confirms their accuracy. The SOTM method is also checked and shows a downside deviation prediction accuracy equivalent to a 750-point Monte Carlo method. From a computation time standpoint, the use of neural networks is required for a reasonable convergence time, and the SOTM used jointly with neural networks results in an optimization time below 1 hour. The proposed approach for making risk/value trade-offs in the presence of multiple risks and objectives is then tested. First, the importance of using downside deviation is demonstrated by showing the risk estimation error made when using the standard deviation rather than the actual downside deviation. Additionally, the use of risk and value scores also helps decision-making from a qualitative and quantitative point of view. Indeed, it facilitates visualization by supplying a two-dimensional Pareto frontier, while still being able to color it to observe program features and cluster patterns. Furthermore, the problem with risk and value scores provides more optimal solutions, compared to the non-aggregated case, unless very large errors in weightings are committed. Finally, the proposed method provides good capabilities for identifying, ranking, and selecting optimal concepts. The last research question presents the following interrogation: does an enterpriselevel approach help improve the optimality of the overall program, and does it result in significantly different decision-making? Two elements of the enterprise-level approach are tested: the integrated optimization, and the use of additional enterprise-level objectives. In both cases, the resulting Pareto frontiers are significantly dominating their counterparts, demonstrating the usefulness of the enterprise-level approach from a quantitative point of view. It also shows that the enterprise-level approach results in significantly different decisions, and should, therefore, be applied early in the design process. Hence, the method provided the capabilities sought in the research objective. This research resulted in contributions in the financial analysis of aerospace programs, in design under multiple sources of uncertainty with multiple objectives, and in design optimization by proposing the adoption of an enterprise-level approach.
GIT SMART: A Feasibility Study of a Mars Scout Vehicle to Study Methane

(Georgia Institute of Technology, 2006-05-02) Kabo, Erik ; Goben, Kathy ; Daskilewicz, Matt ; Deng, Zhi ; Harikanth, Ramraj ; Kim, SoYoung ; Kwon, Kybeom ; Stokes, Kathleen ; Zhang, Daili ; Georgia Institute of Technology. Aerospace Systems Design Laboratory
Ensuring pedestrian safety on campus through the use of computer vision

(Georgia Institute of Technology, 2019-04-26) Commun, Domitille Marie, France ; Mavris, Dimitri N. ; Hunnicutt, Jeffrey Mr ; Pinon Fischer, Olivia Dr ; Balchanos, Michael Dr ; Aerospace Engineering

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
Investigation of nonlinear combustion instability in solid propellant rocket motors by approximate analytical techniques

(Georgia Institute of Technology, 1975) Zinn, Ben T. ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. School of Aerospace Engineering ; Georgia Institute of Technology. Office of Sponsored Programs