Fire Safety Science Digital Archive

This paper critically assesses the experimental tools and optimization techniques that can be applied to determine material pyrolysis properties intended for fire modeling. It is argued that while independent measurement of material pyrolysis properties using multiple specialized laboratory tests may be the most fundamentally correct way to determine these properties, due to practical considerations optimization offers definite advantages for fire modeling and will likely remain an integral part of material pyrolysis property estimation. The performance of four optimization algorithms that have been implemented in Gpyro is assessed in terms of efficiency (how quickly it converges to a solution) and robustness (how close the converged solution is to the global optimum) by extracting 19 material pyrolysis properties from a set of synthetic Cone Calorimeter data. Widely-used genetic algorithm optimization techniques perform poorly in comparison to the Shuffled Complex Evolution (SCE) algorithm, recently applied to material pyrolysis property estimation by Chaos et al. It is shown that SCE consistently converges to the same solution and is capable of reproducing material pyrolysis properties within ~1% of the actual values used to generate the synthetic data set. This work suggests that SCE is capable of determining a unique set of material pyrolysis properties that correspond to the globally optimal solution.