Development of a general learning algorithm with applications to nuclear reactor systems

Author

Carl Ray Brittain

Date of Award

12-1987

Degree Type

Thesis

Degree Name

Master of Science

Major

Nuclear Engineering

Major Professor

Rafael B. Perez

Committee Members

Pedro J. Otaduy

Abstract

The objective of this study is the development of a generalized learning algorithm which could learn to predict a particular feature of a process by observation of a set of representative input examples.

The algorithm uses pattern matching and statistical analysis techniques to find a functional relationship between descriptive attributes of the input examples and the feature to be predicted. The algorithm was tested by applying it to a set of examples consisting of performance descriptions for 277 fuel cycles of Oak Ridge National Laboratory's High Flux Isotope Reactor (HFIR). The program learned to predict the critical rod position for the HFIR from core configuration data prior to reactor startup. The functional found, bases its predictions on initial core reactivity, the number of certain targets placed in the center of the reactor, and the total exposure of the control plates. Twelve characteristic fuel cycle clusters were identified. Nine fuel cycles were diagnosed as having noisy data and one could not be predicted by the functional.

Recommended Citation

Brittain, Carl Ray, "Development of a general learning algorithm with applications to nuclear reactor systems. " Master's Thesis, University of Tennessee, 1987.
https://trace.tennessee.edu/utk_gradthes/13417