• Nuclear Engineering and Technology
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• v.30 no.5
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• pp.452-461
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• 1998

The pin power distribution is determined by analyzing the rotational gamma scanning data for 36 element fuel bundle of HANARO. A fission monitor of Nb$^{95}$ is chosen by considering the criteria of the half-life, fission yield, emitting ${\gamma}$-ray energy and probability. The ${\gamma}$-ray spectra were measured in Korea Atomic Energy Research Institute(KAERI) by using a HPGe detector and by rotating the fuel bundle at steps of 10$^{\circ}$. The counting rates of Nb$^{95}$ 766 keV ${\gamma}$-rays are determined by analyzing the full absorption peak in the spectra. A 36$\times$36 response matrix is obtained from calculating the contribution of each rod at every scanning angle by assuming 2-dimensional and parallel beam approximations for the measuring geometry. In terms of the measured counting rates and the calculated response matrix, an inverse problem is set up for the unknown distribution of activity concentrations of pins. To select a suitable solving method, the performances of three direct methods and the iterative least-square method are tested by solving simulation examples. The final solution is obtained by using the iterative least-square method that shows a good stability. The influences of detection error, step size of rotation and the collimator width are discussed on the accuracy of the numerical solution. Hence an improvement in the accuracy of the solution is proposed by reducing the collimator width of the scanning arrangement.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.954-962
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• 2019

It is well known that the variance of tally is biased in a Monte Carlo calculation based on the power iteration method. Several studies have been conducted to estimate the real variance. Among them, the batch method, which was proposed by Gelbard and Prael, has been utilized actively in many Monte Carlo codes because the method is straightforward, and it is easy to implement the method in the codes. However, there is a problem when utilizing the batch method because the estimated variance varies depending on batch size. Often, the appropriate batch size is not realized before the completion of several Monte Carlo calculations. This study recognizes this shortcoming and addresses it by permitting selection of an appropriate batch size.