Propagation of Model Uncertainty in the Stochastic Simulations of a Compartment Fire

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

Abstract

Model validation and probabilistic simulations are routinely used for quantifying the uncertainties originating from the numerical models and their inputs, respectively. How the two uncertainty types combine in the context of fire risk analyses is not well understood. In this work, we study the propagation of modeling uncertainty to the predicted distributions of probabilistic fire simulations using model validation data representing an uncertain compartment fire scenario. The wall temperatures are predicted in three different ways: one using a coupled model in which the input is the fire heat release rate, and two models using a standalone conduction solver and either experimentally or numerically (CFD) determined heat flux as a boundary condition. Using the predicted wall temperatures, we calculated demonstrative wall failure probabilities assuming different critical threshold temperatures. We propose a simple method for correcting the simulated distributions and probabilities towards the experimentally observed ones. The simulation results with the Fire Dynamics Simulator show that the obtained uncertainties of this particular validation set are similar to the ones reported in the validation guide. In average, the most accurate model over-predicts wall temperature by ∼ 5.0% and the prediction uncertainty for both gas phase and solid phase temperature is ∼ 10%. The wall temperatures predicted from the measured heat-fluxes show higher modeling uncertainty than the ones predicted by a coupled model of the entire gas-wall system. The proposed correction method is shown to improve the accuracy of the predicted distributions for internal wall temperatures at different times. In practical applications, this would lead to more accurate estimates of the time-dependent failure probabilities.

Details

Original languageEnglish
Number of pages28
JournalFire Technology
Publication statusE-pub ahead of print - 14 Mar 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Compartment fire, Modeling uncertainty, Uncertainty propagation

Download statistics

No data available

ID: 32811281