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Completed TRL 4 (started at 2, targeting 4)
Train and test machine-learning model to predict parameters of superheated boiling in a tank from acoustic data in conditions relevant for applications to cryogenic tanks in micro-g. Upon a heat leak applied to a tank wall, local boiling can be initiated, triggering acoustic emission. Machine learning models will be trained on such acoustic data to correlate it to boiling regimes and parameters. The data will be generated in the lab focusing on conditions relevant to space applications, such as high wall superheat and limited convection.
Detection and characterization of boiling at local heat leaks in propellant tanks in micro-g. Early detection and characterization may prevent catastrophic failures
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