TY - JOUR
T1 - Machine Learning for heat radiation modeling of bi- and polydisperse particle systems including walls
AU - Tausendschön, Josef
AU - Stöckl, Gero
AU - Radl, Stefan
N1 - Publisher Copyright:
© 2022 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences
PY - 2023/3
Y1 - 2023/3
N2 - We investigated the ability of four popular Machine Learning methods i.e., Deep Neural Networks (DNNs), Random Forest-based regressors (RFRs), Extreme Gradient Boosting-based regressors (XGBs), and stacked ensembles of DNNs, to model the radiative heat transfer based on view factors in bi- and polydisperse particle beds including walls. Before training and analyzing the predictive capability of each method, an adjustment of markers used in monodisperse systems, as well as an evaluation of new markers was performed. On the basis of our dataset that considers a wide range of particle radii ratios, system sizes, particle volume fractions, as well as different particle-species volume fractions, we found that (i) the addition of particle size information allows the transition from monodisperse to bi- and polydisperse beds, and (ii) the addition of particle volume fraction information as the fourth marker leads to very accurate predictions. In terms of the overall performance, DNNs and RFRs should be preferred compared to the other two options. For particle–particle view factors, DNN and RFR are on par, while for particle–wall the RFR is superior. We demonstrate that DNNs and RFRs can be built to meet or even exceed the prediction quality standards achieved in a monodisperse system.
AB - We investigated the ability of four popular Machine Learning methods i.e., Deep Neural Networks (DNNs), Random Forest-based regressors (RFRs), Extreme Gradient Boosting-based regressors (XGBs), and stacked ensembles of DNNs, to model the radiative heat transfer based on view factors in bi- and polydisperse particle beds including walls. Before training and analyzing the predictive capability of each method, an adjustment of markers used in monodisperse systems, as well as an evaluation of new markers was performed. On the basis of our dataset that considers a wide range of particle radii ratios, system sizes, particle volume fractions, as well as different particle-species volume fractions, we found that (i) the addition of particle size information allows the transition from monodisperse to bi- and polydisperse beds, and (ii) the addition of particle volume fraction information as the fourth marker leads to very accurate predictions. In terms of the overall performance, DNNs and RFRs should be preferred compared to the other two options. For particle–particle view factors, DNN and RFR are on par, while for particle–wall the RFR is superior. We demonstrate that DNNs and RFRs can be built to meet or even exceed the prediction quality standards achieved in a monodisperse system.
KW - Discrete element method (DEM)
KW - Heat radiation modeling
KW - Machine learning
KW - Polydisperse particles
KW - View factors
KW - Wall radiation
UR - http://www.scopus.com/inward/record.url?scp=85132881324&partnerID=8YFLogxK
U2 - 10.1016/j.partic.2022.05.011
DO - 10.1016/j.partic.2022.05.011
M3 - Article
AN - SCOPUS:85132881324
SN - 1674-2001
VL - 74
SP - 119
EP - 140
JO - Particuology
JF - Particuology
ER -