A data driven artificial neural network model for predicting radiative properties of metallic packed beds

Hyun Hee Kang, Mine Kaya, Shima Hajimirza

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)66-72
Number of pages7
JournalJournal of Quantitative Spectroscopy and Radiative Transfer
Volume226
DOIs
StatePublished - Mar 2019

Keywords

  • Metallic packed bed
  • Monte Carlo ray tracing
  • Neural networks
  • Porous media
  • Radiative heat transfer

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