Person:
Sudol,
Alicia
Sudol,
Alicia
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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ItemExploring the Design Space of an Electric Ship using a Probabilistic Technology Evaluation Methodology(Georgia Institute of Technology, 2019-08) McNabb, Jeffrey ; Robertson, Nicole A. ; Steffens, Michael J. ; Sudol, Alicia ; Mavris, Dimitri N. ; Chalfant, JulieWith the advent of new technologies for electric ships, there is a need for a robust methodology to quantitatively evaluate their impact on the performance of a ship, while accounting for the uncertain nature of their parameters. To that end, this paper gives an overview of the Technology Identification, Evaluation, and Selection, or TIES, methodology as applied a 10kton surface combatant. This case study highlights the ability of TIES to aid in a broad exploration of the design space, by giving designers key tools that allow them to show in a traceable manner the tradeoffs involved in infusing technologies and making other design choices, as well as which designs best meet different sets of Figures of Merit. This ultimately allows decision-makers to determine what technologies or design choices to invest in to yield a ship with the performance parameters that will best serve the needs of its stakeholders.
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ItemA methodology for modeling the verification, validation, and testing process for launch vehicles(Georgia Institute of Technology, 2015-11-16) Sudol, AliciaCompleting the development process and getting to first flight has become a difficult hurdle for launch vehicles. Program cancellations in the last 30 years were largely due to cost overruns and schedule slips during the design, development, testing and evaluation (DDT&E) process. Unplanned rework cycles that occur during verification, validation, and testing (VVT) phases of development contribute significantly to these overruns, accounting for up to 75% of development cost. Current industry standard VVT planning is largely subjective with no method for evaluating the impact of rework. The goal of this research is to formulate and implement a method that will quantitatively capture the impact of unplanned rework by assessing the reliability, cost, schedule, and risk of VVT activities. First, the fidelity level of each test is defined and the probability of rework between activities is modeled using a dependency structure matrix. Then, a discrete event simulation projects the occurrence of rework cycles and evaluates the impact on reliability, cost, and schedule for a set of VVT activities. Finally, a quadratic risk impact function is used to calculate the risk level of the VVT strategy based on the resulting output distributions. This method is applied to alternative VVT strategies for the Space Shuttle Main Engine to demonstrate how the impact of rework can be mitigated, using the actual test history as a baseline. Results indicate rework cost to be the primary driver in overall project risk, and yield interesting observations regarding the trade-off between the upfront cost of testing and the associated cost of rework. Ultimately, this final application problem demonstrates the merits of this methodology in evaluating VVT strategies and providing a risk-informed decision making framework for the verification, validation, and testing process of launch vehicle systems.