| Summary: | This thesis examines the theoretical basis for and the experimentation supporting the predictive smoke models currently being used in fire engineering design that is being used, in nationally and internationally accepted guidance documents, to support the increasing use of performance-based building codes/regulations throughout the world. In carrying out this critical examination numerous anomalies are identified between different researcher's results, when considering the same fire environment, and areas where the models put forward in accepted guidance documents have little or no empirical support. This variance between models is demonstrated by the parametric variation of critical data input parameters. To carry out the parametric variation of these input parameters an Excel© calculation system was devised in order to present the information in both graphical and tabular form. The results from the Excel° `experimentation' indicates that the most recent research has resulted in models that predict a lower level of mass smoke flow than the earlier research. It may be suggested that the more recent research, following on and adding to the results of previous researchers produces models that can be used with a greater level of confidence but there is no robust evidence to support this. Currently there is a move towards the use of Computational Fluid Dynamic modelling of fire. However, given the lack of validation of these types of models in the area of smoke movement and the computer time and power required to run these models, there is still a place in fire engineering design for the zone model. It is concluded in this research that, as an increasing number of countries adopt performance building and fire codes/regulations and we lack predictive mass smoke flow models in which regulators, fire engineers and society can have confidence in, the research supporting zone modelling of fire should be extended. It should be carried out in a robust and transparent way in order to either produce models that are substantially more acceptable than those currently being used or to provide more acceptability of current models and their limitations.
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