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Article

Systematic process to determine DNBR limit of CHF correlation with repetitive cross-validation technique

ORCID Icon, , , &
Pages 1034-1042 | Received 13 Nov 2017, Accepted 11 Apr 2018, Published online: 07 May 2018

Figures & data

Figure 1. Flowchart to optimize CHF correlation with cross-validation technique.

Figure 1. Flowchart to optimize CHF correlation with cross-validation technique.

Figure 2. Statistical procedure to yield 95/95 DNBR values.

Figure 2. Statistical procedure to yield 95/95 DNBR values.

Table 1. Sensitivity results for data-set split ratio

Figure 3. Comparison results with repeated k-fold cross-validation technique and sensitivity check on the number of repetition.

Figure 3. Comparison results with repeated k-fold cross-validation technique and sensitivity check on the number of repetition.

Table 2. Statistics of M/Ps for 1000 cases for five classifications

Table 3. Statistics of DNBR values for 1000 cases for five classifications

Table 4. M/P statistics of 39th rank out of 1000 DNBR values

Table 5. Results of the poolablity 39th rank out of 1000 DNBR values

Figure 4. Average M/Ps distribution for 1000 cases for five classifications.

Figure 4. Average M/Ps distribution for 1000 cases for five classifications.

Figure 5. Distribution of 95/95 DNBR values for 1000 cases for five classifications.

Figure 5. Distribution of 95/95 DNBR values for 1000 cases for five classifications.

Table 6. Range of variables for 39th rank out of 1000 DNBR values

Figure 6. Comparison of predicted CHF and measured CHF.

Figure 6. Comparison of predicted CHF and measured CHF.

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