A Bayesian Safety Assessment Methodology for Novel Aircraft Architectures and Technologies using Continuous FHA

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Submitted_Aviation_2019_Bayesian_Safety_Assessment.pdf (1.14 MB)
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Behere, Ameya
Briceno, Simon
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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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http://hdl.handle.net/1853/62534
Abstract
Novel architectures and technologies carry with them an uncertainty related to their reliability and associated safety risk. Existing safety assessment methods involve determining the severity of discrete functional failure and the corresponding probability. However, with the advent of novel aircraft architectural and operational concepts, traditional methods of establishing severity and probabilities failures are found lacking due to the scarcity of available data. The current work proposes a safety assessment method that uses architecture-specific performance models along with continuous functional hazard assessments to inform hazard severity. The probability of failures is determined using a Bayesian framework that does not falter when data is scarce. Taken together, it is expected that this new proposed methodology will enable a more accurate safety assessment of novel aircraft architectures and technologies. A safety assessment of an electric propulsion system powered by a fuel cell is conducted using the proposed methodology to serve as a proof of concept.
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2019-06
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