A framework for data regression of heat transfer data using machine learning

Machine Learning for Chemical Looping: Recent Advances and Prospects

“…While ML–CFD can expedite reactor design, its prediction mechanism does not account for the complex characteristics of fluid and heat transfer. Encouragingly, there has been research to show that ML models can be coupled with physical models, thus enabling predictions to be made while conforming to certain physical laws. ,− If ML can accelerate CFD simulation calculations in the context of learning these objective physical laws, this could substantially facilitate the design and development of chemical-looping-related reactors.…”