The Application of a Genetic Algorithm to Estimate Material Properties for Fire Modeling from Bench-Scale Fire Test Data

摘要

A methodology based on an automated optimization technique that uses a genetic algorithmud(GA) is developed to estimate the material properties needed for CFD-based fire growthudmodeling from bench-scale fire test data. The proposed methodology involves simulating audbench-scale fire test with a theoretical model, and using a GA to locate a set of model parametersud(material properties) that provide optimal agreement between the model predictions and theudexperimental data. Specifically, a genetic algorithm based on the processes of natural selectionudand mutation is developed and integrated with the NIST FDS v4.0 pyrolysis model for thickudsolid fuels. The combined genetic algorithm/pyrolysis model is used with Cone Calorimeter dataudfor surface temperature and mass loss rate histories to estimate the material properties of twoudcharring materials (redwood and red oak) and one thermoplastic material (polypropylene). Thisudis done by finding the parameter sets that provide near-optimal agreement between the modeludpredictions and experimental data given the constraints imposed by the underlying physicaludmodel and the accuracy with which the boundary and initial conditions can be specified. Theudmethodology is demonstrated here with the FDS pyrolysis model and Cone Calorimeter data, butudit is general and can be used with several existing fire tests and almost any pyrolysis model.udAlthough the proposed methodology is intended for use in CFD-based prediction of large-scaleudfire development, such calculations are not performed here and are recommended for futureudwork.