Quantifying Error and Uncertainty in CFAST 2.0 Temperature Predictions

Abstract

In this paper the predictive capability of the smoke transport model CFAST 2.0 is evaluated for five different scenario configurations. The evaluation is made by statistical analysis according to a methodology presented in an earlier paper. Model predictions and experimental data, previously published, are compared and quantitative measures of the predictive capability are thus derived. With the quantitative knowledge of the model error, future predictions from the two-zone model can be adjusted so that the error is taken into account. The suitable method of adjustment depends on how the uncertainty is treated in a specific application. This can be done either by using conservative adjustments or by treating the uncertainty in the predictions as a stochastic variable. The evaluation shows that the statistical method is applicable for this purpose and that reduction of the model error can be achieved with the adjustment model for the types of scenarios subject to analysis.

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