CFD Flame Spread Model Validation: Multi-Component Data Set Framework


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Review of the literature shows that the reported correlation between predictions and experimental data of flame spread vary greatly. The discrepancies displayed by the models are generally attributed to inaccurate input parameters, user effects, and inadequacy of the model. In most experiments, the metric to which the model is deemed accurate is based on the prediction of the heat release rate, but flame spread is a highly complex phenomenon that should not be simplified as such. Moreover, fire growth models are usually made up of distinctive groups of calculation on separate physical phenomena to predict processes that drive fire growth. Inaccuracies of any of these “sub-models” will impact the overall flame spread prediction, hence identifying the sources of error and sensitivity of the subroutines may aid in the development of more accurate models. Combating this issue required that the phenomenon of flame spread be decomposed into four components to be studied separately: turbulent fluid dynamics, flame temperature, flame heat transfer, and condensed phase pyrolysis. Under this framework, aspects of a CFD model may be validated individually and cohesively. However, a lack of comprehensive datasets in the literature hampered this process. Hence, three progressively more complex sets of experiments, from free plume fires to fires against an inert wall to combustible wall fires, were conducted in order to obtain a variety of measurements related to the four inter-related components of flame spread. Multiple permutations of the tests using different source fuels, burner size, and source fire heat release rate allowed a large amount of comparable data to be collected for validation of different fire configurations. FDS simulations using mostly default parameters were executed and compared against the experimental data, but found to be inaccurate. Parametric study of the FDS software shows that there are little definitive trends in the correlation between changes in the predicted quantities and the modeling parameters. This highlights the intricate relationships shared between the subroutines utilized by FDS for calculations related to the four components of flame spread. This reveals a need to examine the underlying calculation methods and source code utilized in FDS.

  • English
  • etd-073012-113336
Defense date
  • 2012
Date created
  • 2012-07-30
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