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On the Use of Neural Network for Evaluation of Total Absorptivity of Glass in a Non-gray Absorbing/Emitting N2/CO2/H2O Mixture Environment

Published

Author(s)

Wai Cheong Tam, Walter W. Yuen

Abstract

Total absorptivity of glass in the presence of a N2/CO2/H2O is determined numerically using the glass spectral optical properties and the spectroscopic data from RADCAL. Results show that mixture properties (surface temperature, mixture temperature, and species concentration) have significant effects on the glass total absorptivity. The commonly used approach of using a gray emitter in the evaluation of the glass total absorptivity is shown to be inaccurate. For a specific glass, a neural network, RADNNET-GL, is generated to correlate the glass total absorptivity and the neural network can be readily applied to practical engineering applications (e.g., fire simulations) to determine the glass thermal behavior efficiently.
Proceedings Title
Eastern States Section of the Combustion Institute
Conference Dates
March 10-13, 2024
Conference Location
Athens, GA, US

Keywords

Radiative heat transfer, Non-gray, Glass, Absorptivity

Citation

Tam, W. and Yuen, W. (2024), On the Use of Neural Network for Evaluation of Total Absorptivity of Glass in a Non-gray Absorbing/Emitting N2/CO2/H2O Mixture Environment, Eastern States Section of the Combustion Institute, Athens, GA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957456 (Accessed April 23, 2024)
Created March 13, 2024, Updated March 17, 2024