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 detector powers. The core design code, Reactor Operation and Control Simulation (ROCS), calculates 3-dimensional power distributions for various core states, 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 not, vert, similar3490 data sets 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 reconstructed axial power shapes of not, vert, similar21,200 cases are compared to the original ROCS axial power shapes. Also, to test the validity and accuracy of the new method, its performance is compared with that of the Fourier fitting method (FFM), a typical method of the deterministic approach. For a total of 21,204 data cases, the averages of root mean square (rms) error, axial peak error (ΔFz), and axial shape index error (ΔASI) of new method are calculated as 0.81%, 0.51% and 0.00204, while those of FFM are 2.29%, 2.37% and 0.00264, respectively. We also evaluated the wide range of axial power profiles from the xenon-oscillation. The results show that the newly developed method is far superior to FFM; average rms and axial peak error are just not, vert, similar35 and not, vert, similar20% of those of FFM, respectively. ©