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.
|Fachzeitschrift||International Journal of Heat and Mass Transfer|
|Publikationsstatus||Veröffentlicht - Okt 2021|
ASJC Scopus subject areas
- !!Condensed Matter Physics
- !!Mechanical Engineering
- !!Fluid Flow and Transfer Processes