The ability to predict fire behaviour of materials is of key interest to building materials industry. The
main reason for it is expensive fire testing and certification costs borne by the manufacturers to bring a finished product to market. Failure in a fire test leads to increased expenses in the product
development cycle leading to delayed realization of profits and low cost competitiveness in the
market. Numerical modelling and fire simulations is a less expensive method to predict the outcomes
of a real fire test. However, the state of the art models existing in literature suffer from several
shortcomings. A few of them are related to inadequacies related to material property data used in
them as input values. Others include modelling deficiencies pertaining to accurate description of
physicochemical processes involved in materials during the fire. Often hurdles in implementation of
appropriate numerical methods are also a cause of poor predictability of mathematical models. In this industrial PhD work, a novel one-dimensional computational pyrolysis model was developed using a combination of deterministic and stochastic approach. The tool is capable of prediction of key fire technical properties of interest obtained in a standard cone calorimeter device such as mass loss rate (MLR), heat release rate (HRR), total heat released (THR). The developed model could be
incorporated into a bigger CFD code and can be used for estimation of fire growth rate on
successively bigger material scale. The performance of novel pyrolysis model considers several
physicochemical transformation complexities occurring in the material and renders a satisfactory
performance of the investigated materials on microscale and bench scale level simulations.
Place: Lecture hall V:B, building V, John Ericssons väg 1, Faculty of Engineering LTH, Lund University, Lund.
Name: Bourbigot, Serge
Affiliation: ENSCL, France.