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Open Access Publications from the University of California

A Generalized Pyrolysis Model for Simulating Charring, Intumescent, Smoldering, and Noncharring Gasification


This paper presents a generalized pyrolysis model that can simulate the gasification of noncharring, charring, and intumescent materials, as well as smoldering in porous media. Separate conservation equations are solved for gaseous and condensed phase mass and species, solid phase energy, and gas-phase momentum. An arbitrary number of gas-phase and condensed-phase species can be accommodated, each having its own temperature-dependent thermophysical properties. The user may specify any number of solid to gas, solid to solid, or solid + gas to solid + gas reactions of any order. Both in-depth radiation transfer through a semi-transparent medium as well as radiation transport across pores are considered, and melting is modeled using an apparent specific heat. All volatiles generated inside the solid escape to the ambient with no resistance to flow unless the pressure solver is invoked to solve for the pressure distribution in the solid, with the resultant flow of volatiles calculated according to Darcy’s law. Similarly, the user may invoke a gas-phase convective-diffusive solver that determines the composition of the volatiles, including diffusion of species from the ambient into the solid. Thus, in addition to calculating the mass-flux of volatiles escaping from the solid, the actual composition of the vapors can be predicted. To aid in determining the required material properties, the pyrolysis model is coupled to a genetic algorithm that can be used to estimate the required input parameters from bench-scale fire tests, thermogravimetric analysis, or a combination thereof. Model predictions are compared to experimental data for the thermo-oxidative decomposition of a non-charring solid (PMMA) and the thermal pyrolysis of a charring solid (white pine), as well as the gasification and swelling of an intumescent coating, and finally smoldering in polyurethane foam. The predictive capabilities of the model are shown to be generally quite good.

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