Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 307-310 |
| Number of pages | 4 |
| Journal | Proceedings of the Thermal and Fluids Engineering Summer Conference |
| Volume | 2023-March |
| State | Published - 2023 |
| Event | 8th Thermal and Fluids Engineering Conference, TFEC 2023 - Hybrid, College Park, United States Duration: Mar 26 2023 → Mar 29 2023 |
ASJC Scopus subject areas
- Condensed Matter Physics
- Mechanical Engineering
- Energy Engineering and Power Technology
- Fluid Flow and Transfer Processes
- Electrical and Electronic Engineering
- Renewable Energy, Sustainability and the Environment
Keywords
- Monte Carlo ray tracing
- Radiative properties
- artificial neural network
- engineering features
- porous media
