Inferring temperature fields from concentration fields in channel flows using conditional generative adversarial networks

Hongfan Cao, Beomjin Kwon, Peter K. Kang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

An accurate estimation of three-dimensional (3D) temperature fields in channel flows is challenging but critical for many important applications such as heat exchangers, radiation energy collectors, and enhanced geothermal systems. In this paper, we demonstrate the possibility of inferring temperature fields from concentration fields for laminar convection flows in a 3D channel using a machine learning (ML) approach. The study involves generation of data using 3D numerical simulations, application of deep learning methodology using conditional generative adversarial networks (cGANs), and analysis of how dataset selection affects model performance. The model is also tested for applicability in different convection scenarios. Results show that cGANs can successfully infer temperature fields from concentration fields, and the reconstruction accuracy is sensitive to the training dataset selected. In this study, we demonstrate how ML can be used to overcome the limitations of traditional heat and mass analogy functions widely used in heat transfer research.

Original languageEnglish (US)
Article number214701
JournalJournal of Applied Physics
Volume135
Issue number21
DOIs
StatePublished - Jun 7 2024

Bibliographical note

Publisher Copyright:
© 2024 Author(s).

Fingerprint

Dive into the research topics of 'Inferring temperature fields from concentration fields in channel flows using conditional generative adversarial networks'. Together they form a unique fingerprint.

Cite this