타이틀 |
The Application of Neural Networks to the SSME Startup Transient |
저자 |
Claudia M. Meyer and William A. Maul |
Keyword |
Neural nets; Space shuttle main engine; Test firing |
URL |
http://gltrs.grc.nasa.gov/reports/1991/CR-187138.pdf |
보고서번호 |
NASA CR-187138 |
발행년도 |
1991 |
출처 |
NASA Glenn Research Center |
ABSTRACT |
Feedforward 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 failureindicating 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.
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