Title page for ETD etd-12042009-020108
|Type of Document
||Adams, Joseph T.
||Neural network calibration of moderator temperature coefficient measurements in pressurized water nuclear reactors
||Master of Science
|Thomas, James R. Jr.
|Pierce, Felix J.
|Wicks, Alfred L.
|Date of Defense
Neural networks have been shown to be capable of predicting the moderator temperature
coefficient in a nuc1ear reactor by using the frequency response functions between the in-core
neutron flux signal and the ex-core thermocouple signal as inputs. In this work, actual data from
a nuc1ear reactor is used by neural networks to estimate the moderator temperature coefficient at
different times during a fuel cycle. Along with the conventional method of training neural
networks, a new method of training that better models the use of neural networks in predicting
the moderator temperature coefficient is also successfully demonstrated. The results show that
neural networks are effective at estimating the moderator temperature coefficient if the domain
of prediction is within the training domain of the network. The advantage of using the
autoregression method to create the frequency response patterns used as inputs to the neural
network as opposed to frequency response functions calculated by the Fourier transform method
is also shown.
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