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Variables close together have a positive correlation, variables in the opposite direction are negatively correlated, and orthogonal variables are independent of each other.
The optimal local correlation variables for very low Re flows were found after a lengthy calibration process.
To calibrate the transition model for wind turbine applications, a long process of optimizing the local correlation variables was carried out.
Figure 2 Surface detailed visual analysis of under ROC curve area with validation and test sample set, for SVM analysis for all preceding models after high correlation variables removal.
However, once having calibrated the model for some Reynolds numbers, the optimal parameters may be extrapolated for any other Reynolds number within the range of optimization, by interpolating the correlation variables with polynomial functions.
(6) These local correlation variables were calibrated in the original model by implementing functions obtained through the use of experimental data, carried out for flat plates and gas turbine blades [14, 15, 16, 30, 36].
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High-correlation variables removal, SVM machine learning algorithm, and surface area ROC analysis were also applied.
Table 3 Correlation matrix Correlations Variables 1 2 3 4 5 6 7 8 9 10 1. System quality 1 2.
Table 2 Means, Standard Deviations, and Correlations Variables Mean SD 1 2 3 4 5 6 7 8 1. Age 2.05.98 2.05.98
Table 1 Means, standard deviations, and correlations Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 1.
Table 1 Means, standard deviations, and correlations Variable M SD 1 2 3 4 5 6 7 8 9 1.
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