Comparison of spatial interpolation approaches for in-core power distribution reconstruction

Abstract

Abstract This paper investigated the influence of various types of spatial interpolation algorithms in the reactor in-core power distribution reconstruction. These algorithms include different kinds of kernel function used in radial basis function (RBF) methods or support vector machine regression (SVR) methods, different orders of polynomial trend surface analysis (TSA), and various forms of distance weight average (DWA) methods, geo-statistics interpolation method. A typical pressurized water reactor core with 157 fuel assemblies and 33 measurement instruments located is analyzed. The validations of these methods under the measurement core status and predictive core status have been provided. The criterions of relative root mean square error (RRMSE) have been applied to guarantee the accuracy of these algorithms. The comparison of the different spatial interpolation algorithms shows that the DWA basis methods usually perform much better and stable than other methods. PEM and SVR methods have very poor performance in high signal deviation situation, but they can effectively eliminate measurement error in the opposite situation. The fitting methods TPS0, PEM4 could not be used in in-core power distribution reconstruction (IPDR). TPS1 is the best choice for no parameter RBF methods. While for other RBF basis methods, optimization algorithm should be used to search the optimized model parameters. The performance of RBF_TPS1, DWA_OK, SVR_Gauss, RBF_Gauss fall into a same group. Three-dimension surfaces of fitting results are compared. The factors are discussed that affect the reconstructed results of the methods, including detectors number, detectors design pattern and detector measurement properties, variability of the fitting surface. Suggestions to select an appropriate spatial interpolator method are provided.