Kelli McCoy began her career at NASA Kennedy Space Center as an Industrial Engineer in the Launch Services Program, following her graduation from Georgia Tech with a M.S in Industrial and Systems Engineering. She went on to obtain a M.S. in Applied Math and Statistics at Georgetown University, and subsequently developed probability models to estimate cost and schedule during her tenure with the Office of Evaluation at NASA Headquarters. Now at Jet Propulsion Laboratory, she has found applicability for math and probability models in an engineering environment. She further developed that skillset as the Lead of the Europa Clipper Project Systems Engineering Analysis Team, where she and her team produced 3 probabilistic risk assessments for the mission, using their model-based SE environment. She is currently the modeling lead for a JPL New Frontiers proposal and is a member of JPL’s Quantification of Uncertainty Across Disciplines (QUAD) team, which is promoting Uncertainty Quantification practices across JPL. In parallel, Kelli is pursuing a PhD in UQ at University of Southern California. Co-author, Roger Ghanem is a Professor Civil Engineering at the University of Southern California
In space mission formulation, it is critical to link the scientific phenomenology under investigation directly to the spacecraft design, mission design, and the concept of operations. With many missions of discovery, the large uncertainty in the science phenomenology and the operating environment necessitates mission architecture solutions that are robust and resilient to these unknowns, in order to maximize the probability of achieving the mission objectives. Feasible mission architectures are assessed against performance, cost, and risk, in the context of large uncertainties. For example, despite Cassini observations of Enceladus, significant uncertainties exist in the moon’s jet properties and the surrounding Enceladus environment. Orbilander or any other mission to Enceladus will need to quantify or bound these uncertainties in order to formulate a viable design and operations trade space that addresses a range of mission objectives within the imposed technical and programmatic constraints. Uncertainty quantification (UQ), utilizes a portfolio of stochastic, data science, and mathematical methods to characterize uncertainty of a system and inform risk and decision-making. This discussion will focus on a formulation of a UQ workflow and an example of an Enceladus mission development use case.
29 июн 2023
