The application of neural networks to the SSME startup transientFeedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter, the high pressure oxidizer turbine discharge temperature, a redlined parameter, and the high pressure fuel pump discharge pressure, a failure-indicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set.
Document ID
19910059660
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Meyer, Claudia M. (NASA Lewis Research Center Cleveland, OH, United States)
Maul, William A. (NASA Lewis Research Center; Sverdrup Technology, Inc. Brook Park, OH, United States)