Corrosion Shape Identification in Nuclear Reactor Tube Using Inverse Applied Electromagnetic Analysis

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Research has been successfully implemented for corrosion shape identification in nuclear reactor tubes with magnetic measurement data. With the background knowledge of inverse analysis in computational mechanics, feasible estimation algorithms have been proposed for quantitative nondestructive evaluations related to nuclear reactor tubes. First, the magnetic inspection process was described by the precise mathematical model based on Maxwell's equations. Simultaneously, corrosion shapes were categorized into some class of geometrical structures and approximated by appropriate bases functions. Secondly, computational schemes were doveloped for estimating the corrosion shape in sample materials using the ideas of nonlinear optimizations, including the regularization techniques, the sensitivity analysis and the genetic algorithm, etc. Proposed algorithms were tested both with simulation data and with laboratory data. Results obtained here are summarized as follows :(1) Magnetic Inverse Ana … More lysis by L-curve Approach : Measuring magnetic flux densities, the inverse problem considered here was aimed at recovering the eddy current vector potentials. Since this problem becomes ill-posed, the regularization technique which is called L-curve method was proposed.(2) Parameter Estimation for Crack Depth Reconstruction : Motivated by eddy current inspection for steam generator tubes, inverse problems of crack depth reconstruction method were considered. Material flaws can be detected using a probe impedance trajectory which is described by hybrid FEM-BEM scheme. A parameter estimation method was presented for reconstructing the shape of the crack depth. Results of experiments with laboratory data were successfully tested.(3) Material Degradation using Genetic Algorithm : An inverse computational algorithm was developed for estimating the location and number of voids inside magnetic materials. The genetic algorithm was used in order to demonstrate the feasibility of the proposed algorithm. Less