TY - JOUR
T1 - A GENERALIZED CHARACTERIZATION OF RADIATIVE PROPERTIES OF A POROUS MEDIA USING ENGINEERED FEATURES AND NEURAL NETWORK
AU - Eghtesad, Amirsaman
AU - Tabasum, Farhin
AU - Hajimirza, Shima
N1 - Publisher Copyright:
© 2023 Begell House Inc.. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The assessment of radiative properties of materials and devices at high-temperature levels is an imperative and inevitable part of various engineering applications. Particularly, the study of radiative heat transfer in porous media has attracted the attention of researchers for decades. Advanced computational algorithms have provided a reliable approach to tackle the cumbersomeness of experimental measurements. In this study, Monte Carlo ray tracing (MCRT) method is implemented to generate supervised labeling data for random overlapping and non-overlapping circular packed beds by solving the classical radiative transfer equation. A highly generic artificial neural network (ANN) model based on engineered physical and geometrical features is designed to predict the radiative properties of a given arbitrary porous media at significantly lower computational costs. Results demonstrate the generalizability and applicability of the present model for the calculation of radiative characteristics of porous media.
AB - The assessment of radiative properties of materials and devices at high-temperature levels is an imperative and inevitable part of various engineering applications. Particularly, the study of radiative heat transfer in porous media has attracted the attention of researchers for decades. Advanced computational algorithms have provided a reliable approach to tackle the cumbersomeness of experimental measurements. In this study, Monte Carlo ray tracing (MCRT) method is implemented to generate supervised labeling data for random overlapping and non-overlapping circular packed beds by solving the classical radiative transfer equation. A highly generic artificial neural network (ANN) model based on engineered physical and geometrical features is designed to predict the radiative properties of a given arbitrary porous media at significantly lower computational costs. Results demonstrate the generalizability and applicability of the present model for the calculation of radiative characteristics of porous media.
KW - Monte Carlo ray tracing
KW - Radiative properties
KW - artificial neural network
KW - engineering features
KW - porous media
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M3 - Conference article
AN - SCOPUS:85171273323
VL - 2023-March
SP - 307
EP - 310
JO - Proceedings of the Thermal and Fluids Engineering Summer Conference
JF - Proceedings of the Thermal and Fluids Engineering Summer Conference
T2 - 8th Thermal and Fluids Engineering Conference, TFEC 2023
Y2 - 26 March 2023 through 29 March 2023
ER -