ESTIMATING RADIATIVE PROPERTIES IN ARBITRARY POROUS MEDIA USING CASE-SPECIFIC DATA – DRIVEN MACHINE LEARNING FRAMEWORKS

Farhin Tabassum, Amirsaman Eghtesad, George Rafael Domenikos, Shima Hajimirza

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Monte Carlo ray tracing (MCRT) has been a widely implemented and reliable computational method to calculate light-matter interactions in porous media. However, the computational modeling of porous media and performing MCRT becomes significantly costly while dealing with an intricate porous structure and numerous dependent variables. Hence, supervised machine learning (ML) models have been used to estimate the radiative properties. While high estimation accuracy is important, it is also crucial choosing the optimal Machine Learning framework based on the hierarchy of datasets, and the methodology used to pack the particles in a packed bed. The first model is a Gaussian Process (GP) model for a pack-free MCRT method, where a monodispersed spherical packed bed is studied, and it addresses the geometric complexity with the corresponding value of the penetration length probability distribution. The second model is a low-cost, physical, and geometrical-feature-based Artificial Neural Network (ANN) appropriate for pack-based MCRT method, used to study an overlapping circle-packed bed with lower porosity. This study reveals that these case-specific data-driven machine-learning frameworks are highly effective in predicting radiative properties in porous media, providing a promising tool for further research in this field.

Original languageEnglish
Title of host publicationProceedings of the 10th International Symposium on Radiative Transfer, RAD 2023
Pages119-126
Number of pages8
ISBN (Electronic)9781567005318
DOIs
StatePublished - 2023
Event10th International Symposium on Radiative Transfer, RAD 2023 - Thessaloniki, Greece
Duration: 12 Jun 202316 Jun 2023

Publication series

NameProceedings of the International Symposium on Radiative Transfer
Volume2023-June
ISSN (Electronic)2642-5629

Conference

Conference10th International Symposium on Radiative Transfer, RAD 2023
Country/TerritoryGreece
CityThessaloniki
Period12/06/2316/06/23

Fingerprint

Dive into the research topics of 'ESTIMATING RADIATIVE PROPERTIES IN ARBITRARY POROUS MEDIA USING CASE-SPECIFIC DATA – DRIVEN MACHINE LEARNING FRAMEWORKS'. Together they form a unique fingerprint.

Cite this