Model Considerations for Fire Scene Reconstruction Using a Bayesian Framework
| Autor: | Andrew Kurzawski, Jan-Michael Cabrera, Ofodike A. Ezekoye |
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| Rok vydání: | 2019 |
| Předmět: |
040101 forestry
Computer science Bayesian probability 020101 civil engineering Inversion (meteorology) 04 agricultural and veterinary sciences 02 engineering and technology 0201 civil engineering Posterior mean symbols.namesake Surrogate model Fire Dynamics Simulator Markov chain monte carlo sampling symbols 0401 agriculture forestry and fisheries General Materials Science Bayesian framework Safety Risk Reliability and Quality Gaussian process Algorithm |
| Zdroj: | Fire Technology. 56:445-467 |
| ISSN: | 1572-8099 0015-2684 |
| DOI: | 10.1007/s10694-019-00886-w |
| Popis: | Towards the development of a more rigorous approach for coupling collected fire scene data to computational tools, a Bayesian computational strategy is presented in this work. The Bayesian inversion technique is exercised on synthetic, time-integrated data to invert for the location, size, and time-to-peak of an unknown fire using two well-known forward models; Consolidated Model of Fire and Smoke Transport (CFAST) and Fire Dynamics Simulator (FDS). A Gaussian process surrogate model was fit to coarse FDS simulations to facilitate Markov Chain Monte Carlo sampling. The inversion framework was able to predict the total energy release by all fire cases except for one CFAST forward model, a 1000 kW steady fire. It was found that insufficient information was available in the time-integrated data to distinguish the temporal variations in peak times. FDS performed better than CFAST in predicting the maximum energy release rate with the posterior mean of the best configurations being 0.05% and 2.77% of the true values respectively. Both models performed equally well on locating the fire in a compartment. |
| Databáze: | OpenAIRE |
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