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Using experimental data, a prior model evaluation is performed to assess critical modeling assumptions regarding the applicability of this formulation to partially-premixed combustion, the statistical representation of the scalar mixing, and the joint PDF-closure.
The model evaluation is carried out through 18 case studies.
Model evaluation is a central point for developers.
Model evaluation is an essential milestone of model development to increase confidence in the predictions.
Model evaluation is done using two economic criteria and two statistical tests.
A multi-criteria model evaluation is developed to select models that balance runoff efficiency with selection of accurate parameter values.
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In addition to the sensitivity analyses detailed in this section, a more formal model evaluation was performed to ensure the newly developed modeling system was performing as expected.
Model evaluation was conducted via comparing exposure and dose-modeling predictions against duplicate diet data and biomarker measurements, respectively, for the same individuals.
Model evaluation was done using the adjusted R-square of the regression.
Moreover, model evaluation was carried out by assessing the linear correlation between measured and predicted values.
Model evaluation was then carried out via assessing the linearity between the experimental data and predicted values, which proved the applicability of the two models.
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