A methodology based on an automated optimization technique that uses a genetic algorithm
(GA) is developed to estimate the material properties needed for CFD-based fire growth
modeling from bench-scale fire test data. The proposed methodology involves simulating a
bench-scale fire test with a theoretical model, and using a GA to locate a set of model parameters
(material properties) that provide optimal agreement between the model predictions and the
experimental data. Specifically, a genetic algorithm based on the processes of natural selection
and mutation is developed and integrated with the NIST FDS v4.0 pyrolysis model for thick
solid fuels. The combined genetic algorithm/pyrolysis model is used with Cone Calorimeter data
for surface temperature and mass loss rate histories to estimate the material properties of two
charring materials (redwood and red oak) and one thermoplastic material (polypropylene). This
is done by finding the parameter sets that provide near-optimal agreement between the model
predictions and experimental data given the constraints imposed by the underlying physical
model and the accuracy with which the boundary and initial conditions can be specified. The
methodology is demonstrated here with the FDS pyrolysis model and Cone Calorimeter data, but
it is general and can be used with several existing fire tests and almost any pyrolysis model.
Although the proposed methodology is intended for use in CFD-based prediction of large-scale
fire development, such calculations are not performed here and are recommended for future
work
