Bias factor method using random sampling technique

Toward the practical use of the bias factor method for actual light water reactor core analyses, the bias factor method using the random sampling technique is newly proposed. The bias factor method is one of the correction methods using information of E/C values in existing measurable systems, to reduce biases and uncertainties of predicted core characteristics parameters. By the aid of the random sampling technique, our proposed bias factor method can be carried out using only forward calculations without any adjoint calculations, and can easily take into account burnup and thermal-hydraulic feedback effects, which are difficult points in the practical application to actual core analyses. Although the statistical error due to the random sampling technique is inevitable in the proposed method, the statistical error can be simply quantified by the resampling technique such as the bootstrap method. As one of the feasibility studies, effectiveness of the proposed method is verified through a numerical experiment which virtually simulates a typical equilibrium pressurized water reactor core. In this verification problem, it is clarified that E/C values of control rod worth at the beginning of cycle under the hot zero power condition are useful information to reduce biases and uncertainties of predicted assembly-wise power distributions during operation of hot full power.journal articl

高機能・環境に配慮した繊維材料およびその加工法に関する研究

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Bias factor method using random sampling technique

Toward the practical use of the bias factor method for actual light water reactor core analyses, the bias factor method using the random sampling technique is newly proposed. The bias factor method is one of the correction methods using information of E/C values in existing measurable systems, to reduce biases and uncertainties of predicted core characteristics parameters. By the aid of the random sampling technique, our proposed bias factor method can be carried out using only forward calculations without any adjoint calculations, and can easily take into account burnup and thermal-hydraulic feedback effects, which are difficult points in the practical application to actual core analyses. Although the statistical error due to the random sampling technique is inevitable in the proposed method, the statistical error can be simply quantified by the resampling technique such as the bootstrap method. As one of the feasibility studies, effectiveness of the proposed method is verified through a numerical experiment which virtually simulates a typical equilibrium pressurized water reactor core. In this verification problem, it is clarified that E/C values of control rod worth at the beginning of cycle under the hot zero power condition are useful information to reduce biases and uncertainties of predicted assembly-wise power distributions during operation of hot full power.journal articl

Results from the 12 CDSs collinear with the L-strand and ND6 collinear with the H-strand, with results from individual paired-sample t-tests between P_UUA and P_AUA.

<p>Results from the 12 CDSs collinear with the L-strand and ND6 collinear with the H-strand, with results from individual paired-sample t-tests between P_UUA and P_AUA.</p

Figure 1

<p>Genomic reduction of AUA codons is associated with a reduction in methionine usage. P_AUA is defined in equation (1) and arcscine-transformed; N_Met – Number of methionine codons.</p

Results from the 13 CDSs from the four urochordate species, <i>Halocynthia roretzi, Ciona intestinalis, Ciona savignyi</i>, and <i>Doliolum nationalis</i>, whose mitochondrial genomes each have a UAU-tRNA^Met gene in addition to a CAU-tRNA^Met gene.

<p>Results from the 13 CDSs from the four urochordate species, <i>Halocynthia roretzi, Ciona intestinalis, Ciona savignyi</i>, and <i>Doliolum nationalis</i>, whose mitochondrial genomes each have a UAU-tRNA^Met gene in addition to a CAU-tRNA^Met gene.</p

The effect of anticodons (AC) of the two tRNA-Met genes in bivalve mitochondrial genomes on P_UUA and P_AUA.

<p>Those with only CAU-tRNA^Met genes have reduced AUA usage than those with both CAU-tRNA^Met and UAU-tRNA-^Met genes.</p

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