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Assessment of Radiation Solver of Fire Simulation Models Using RADNNET-ZM

Published

Author(s)

Wai Cheong Tam, Walter W. Yuen

Abstract

The paper presents a neural-network based zonal method (RADNNET-ZM) for the analysis of radiative heat transfer in an arbitrary Cartesian enclosure with an isothermal, inhomogeneous, non-gray medium. The model accounts for the non-gray effect of absorbing species in a combustion environment and the geometric effect of any three-dimensional enclosures. The model is verified against benchmark solutions. Maximum local error is observed to be less than 4 %. Prediction accuracy of an existing zonal radiation solver is assessed. Results demonstrate that RADNNET-ZM can provide substantial improvement to zone fire simulation models for the prediction of radiative heat transfer without a significant increase in computation cost.
Proceedings Title
11th Asia-Oceania Symposium on Fire Science and Technology (AOSFST)
Conference Dates
October 21-25, 2018
Conference Location
Taipei

Keywords

Neural network, zonal method, non-gray, multi-dimensional, fire simulation model

Citation

, W. and Yuen, W. (2019), Assessment of Radiation Solver of Fire Simulation Models Using RADNNET-ZM, 11th Asia-Oceania Symposium on Fire Science and Technology (AOSFST), Taipei, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925079 (Accessed June 15, 2024)

Issues

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Created September 13, 2019, Updated September 23, 2019