American Nuclear Society
Home

Home / Store / Journals / Electronic Articles / Nuclear Science and Engineering / Volume 139 / Number 1 / Pages 96-104

Genetic Algorithms for Estimating Effective Parameters in a Lumped Reactor Model for Reactivity Predictions

Marzio Marseguerra, Enrico Zio

Nuclear Science and Engineering / Volume 139 / Number 1 / Pages 96-104

September 2001

Format:

Price:$30.00
Member Price:$27.00
Member Savings:$3.00

The control system of a reactor should be able to predict, in real time, the amount of reactivity to be inserted (e.g., by control rod movements and boron injection and dilution) to respond to a given electrical load demand or to undesired, accidental transients. The real-time constraint renders impractical the use of a large, detailed dynamic reactor code. One has, then, to resort to simplified analytical models with lumped effective parameters suitably estimated from the reactor data.

The simple and well-known Chernick model for describing the reactor power evolution in the presence of xenon is considered and the feasibility of using genetic algorithms for estimating the effective nuclear parameters involved and the initial nonmeasurable xenon and iodine conditions is investigated. This approach has the advantage of counterbalancing the inherent model simplicity with the periodic reestimation of the effective parameter values pertaining to each reactor on the basis of its recent history. By so doing, other effects, such as burnup, are automatically taken into account.

 
 
 
Questions or comments about the site? Contact the ANS Webmaster.
advertisement