TY - JOUR
T1 - A data driven artificial neural network model for predicting radiative properties of metallic packed beds
AU - Kang, Hyun Hee
AU - Kaya, Mine
AU - Hajimirza, Shima
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/3
Y1 - 2019/3
N2 - In this work, we demonstrate that machine learning methods can be reliably used to predict radiative properties of dispersed media, i.e. packed beds, as a function of packed bed geometry and material properties. The computationally expensive Monte Carlo ray tracing (MCRT) method, which is widely used in this context, is replaced by Neural Networks (NN). We demonstrate that the data-driven surrogate prediction works accurately and generally. The results of both MCRT and NN models agree well with each other and with previously measured literature results. We also measure the uncertainty of the NN results using statistical methods. It is recommended that the developed model be used for efficient inverse problems and optimizations in relevant future work.
AB - In this work, we demonstrate that machine learning methods can be reliably used to predict radiative properties of dispersed media, i.e. packed beds, as a function of packed bed geometry and material properties. The computationally expensive Monte Carlo ray tracing (MCRT) method, which is widely used in this context, is replaced by Neural Networks (NN). We demonstrate that the data-driven surrogate prediction works accurately and generally. The results of both MCRT and NN models agree well with each other and with previously measured literature results. We also measure the uncertainty of the NN results using statistical methods. It is recommended that the developed model be used for efficient inverse problems and optimizations in relevant future work.
KW - Metallic packed bed
KW - Monte Carlo ray tracing
KW - Neural networks
KW - Porous media
KW - Radiative heat transfer
UR - http://www.scopus.com/inward/record.url?scp=85060043741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060043741&partnerID=8YFLogxK
U2 - 10.1016/j.jqsrt.2019.01.013
DO - 10.1016/j.jqsrt.2019.01.013
M3 - Article
AN - SCOPUS:85060043741
SN - 0022-4073
VL - 226
SP - 66
EP - 72
JO - Journal of Quantitative Spectroscopy and Radiative Transfer
JF - Journal of Quantitative Spectroscopy and Radiative Transfer
ER -