Application of Neural Networks to Analyze Load-Follow Operation in a Pressurized Water Reactor

A new analytic model based on hidden-layer neural networks is designed to analyze load-follow operation in a pressurized water reactor (PWR). The new model is mainly made up of four error backpropagation neural networks and procedures to calculate core parameters such as k and xenon distributions in a transient core. The first two neural networks are designed to retrieve the power distribution, the third is for axial offset, and the fourth is for reactivity corresponding to a given core condition. The training data sets are generated by three-dimensional nodal code and the measured data of the first-day load-follow operation. The simulation results of the 5-day load-follow test in a PWR using the new analytic model show that it is an attractive tool for plant simulations in terms of accuracy, computing time, cost, and adaptability to measurements.