Nuclear Technology Laboratory
Annals of Nuclear Energy paper abstract.
Volume 29, Issue 3 , February 2002 , Pages 235-253
Instability localization with artificial neural networks (ANNs).
T. Tambouratzis, and M. Antonopoulos-Domis
The aim of this piece of research is to investigate the potential of
artificial neural networks (ANNs) for tackling the problem of instability
localization. The instability is modeled by a variable strength absorber
(point-source) in a two-dimensional bare reactor model with a one neutron-energy
group. The proposed approach constitutes an exercise in simplicity in
that: (1) an arbitrarily simplified model is employed for ANN training
and validation; (2) few training and validation patterns of low complexity
are utilized; (3) the ANN inputs are derived directly from the neutron
noise signals, the proposed location of instability is given on-line via
an uncomplicated combination of ANN outputs; (4) the ANN architecture
is independent of the number of possible locations of instability. In
fact, unlike previous approaches which employ hundreds of outputs (one
for each fuel assembly), only two ANN outputs are employed representing
the X- and Y-coordinates (location) of instability; (5) the responses
of only a few detectors are employed; (6) a measure of confidence in the
prediction is assigned. The results of ANN testing, which is performed
on patterns from both actual and simplified models, are reported and analyzed.