Reconstruction of core axial power shapes using the alternating conditional expectation algorithm

We have introduced the alternating conditional expectation (ACE) algorithm in reconstructing 20-node axial core power shapes from five-level in-core d etector powers. The core design code, Reactor Operation and Control Simulat ion (ROCS), calculates 3-dimensional power distributions for various core s tates, and the reference core-averaged axial power shapes and corresponding simulated detector powers are utilized to synthesize the axial power shape . By using the ACE algorithm, the optimal relationship between a dependent variable, the plane power, and independent variables, five detector powers, is determined without any preprocessing. A total of similar to 3490 data s ets per each cycle of YongGwang Nuclear (YGN) power plant units 3 and 4 is used for the regression. Continuous analytic function corresponding to each optimal transformation is calculated by simple regression model. The recon structed axial power shapes of similar to 21,200 cases are compared to the original ROCS axial power shapes. Also, to test the validity and accuracy o f the new method, its performance is compared with that of the Fourier fitt ing method (FFM), a typical method of the deterministic approach. For a tot al of 21,204 data cases, the averages of root mean square (rms) error, axia l peak error (Delta F-z), and axial shape index error (Delta ASI) of new me thod are calculated as 0.81%, 0.51% and 0.00204, while those of FFM are 2.2 9%, 2.37% and 0.00264, respectively. We also evaluated the wide range of ax ial power profiles from the xenon-oscillation. The results show that the ne wly developed method is far superior to FFM; average rms and axial peak err or are just similar to 35 and similar to 20% of those of FFM, respectively. (C) 1999 Elsevier Science Ltd. All rights reserved.