Deep neural network-based heat radiation modelling between particles and between walls and particles

Josef Franz Viktor Tausendschön*, Stefan Radl

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

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.

Originalspracheenglisch
Aufsatznummer121557
FachzeitschriftInternational Journal of Heat and Mass Transfer
Jahrgang177
DOIs
PublikationsstatusVeröffentlicht - Okt 2021

ASJC Scopus subject areas

  • !!Condensed Matter Physics
  • !!Mechanical Engineering
  • !!Fluid Flow and Transfer Processes

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