Abstract
We present a Deep Neural Network (DNN)-based view factor model to calculate radiative heat transfer rates between particles, as well as between particles and walls in Discrete Element Method (DEM)-based simulations. A systematic analysis of the most promising markers available in DEM simulations to be correlated with the view factor is performed. Subsequently, a neural network is trained, and its predictive performance is analyzed. View factors are studied for a variety of systems ranging from dilute particle systems to dense (i.e., settled under gravity) particle beds. It is demonstrated that the trained DNN-model can model view factors at higher accuracy and with significantly less computational effort than other literature models. A validation with experimental data is provided, showing that the implemented model can predict the total heat flux, as well as particle temperatures in a packed bed accurately.
Original language | English |
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Article number | 121557 |
Journal | International Journal of Heat and Mass Transfer |
Volume | 177 |
DOIs | |
Publication status | Published - Oct 2021 |
Keywords
- Deep Neural Networks
- Discrete Element Method (DEM)
- Machine learning
- Radiation modelling
- Radiative heat transfer
- View factors
- Wall radiation
ASJC Scopus subject areas
- Condensed Matter Physics
- Mechanical Engineering
- Fluid Flow and Transfer Processes