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Inferring and Propagating Kinetic Parameter Uncertainty for Condensed Phase Burning Models

Published

Author(s)

Morgan C. Bruns

Abstract

Kinetic parameters for serial pyrolysis reactions were calibrated from thermo- gravimetric analysis (TGA) data using Bayesian inference via Markov Chain Monte Carlo (MCMC) simulations. The resulting inferences are probabilistic as opposed to the point estimates calibrated in previous studies and are visualized using posterior probability density functions (PDFs) generated by kernel density estimation (KDE). To evaluate the effect of this uncertainty on predictions of burning rate, samples from the posterior were used to simulate gasification and cone calorimetry experiments using the Fire Dynamics Simulator (FDS). It is argued that the proposed methodology is necessary for progress in modeling of condensed phase physics for fire problems as it supports both model validation and engineering practice.
Citation
Fire Technology

Keywords

Pyrolysis, Kinetics, TGA, Bayesian Inference, Markov Chain Monte Carlo, Uncertainty Propagation, FDS

Citation

Bruns, M. (2015), Inferring and Propagating Kinetic Parameter Uncertainty for Condensed Phase Burning Models, Fire Technology, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=917055 (Accessed May 28, 2024)

Issues

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Created February 9, 2015, Updated February 19, 2017