(A) study on determination of optimum data points for scaling factor calculation = 척도 인자 계산에서의 최적화 데이터 수 결정 방법에 관한 연구

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An accurate radionuclide inventory of Low-level wastes (LLW) prior to transport for disposal is important. However, representative sampling of waste packages poses a radiological hazard to personnel and is also difficult and expensive to obtain. Establishing correlation relationships between the concentration of the difficult-to-measure (DTM) nuclides and the easy-to-measure (Key) nuclides, called scaling factor, is proposed to meet the need for inventory description. Scaling factors are calculated based on a database from radiochemical analyses of representative waste samples. Several data points are needed to derive a reliable scaling factor. The more the number of data points, the better is the correlation, but more costly because of number of needed radiochemical analyses. Therefore, optimization of data points should be considered to minimize the cost without compromising reliability and prediction of the scaling factor. Scaling factors for Ni-63, Sr-90, and C-14 were calculated using Co-60 and Cs-137 as Key nuclides based on the published data in EPRI-4037. Data points were treated in two ways: non-segregated (all data points) and segregated according to waste streams, which were qualitatively compared. Correlation coefficient, percent error and relative standard deviation were plotted against the number of data points used in the estimation of scaling factor. The optimum number of data points was obtained to where there was no significant improvement in the statistical uncertainties by using additional samples. It was found that 54 data points were sufficient to characterize Ni-63, while 100 data points were sufficient to characterize C-14. It was also found that 14 data points from RCS waste stream were sufficient to characterize Sr-90. Results showed that, segregation of data points according to waste streams may be an option to characterize scaling factor. On the other hand, non-segregation of data points is not an option for Sr-90 since it is b...
Lee, Kun-Jairesearcher이건재researcher
한국과학기술원 : 원자력및양자공학과,
Issue Date
264908/325007  / 020054306

학위논문(석사) - 한국과학기술원 : 원자력및양자공학과, 2007.2, [ vi, 43 p. ]


scaling factor; optimum data point; 최적화 데이터 수; 척도인자

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