A Methodology for Simulating Supercritical CO2 Heat Transfer Experiments Using Machine Learning Models

Abstract

In an effort to support the growth of supercritical carbon dioxide (sCO2) power cycles in the energy industry, this study seeks to train a machine learning model to mirror experimental data to inform future efforts and design features for both sCO2 heat exchangers and sCO2 turbine thermal management. There is a need for large amounts of experimental testing as there is less established literature about sCO2 used as a working medium in these cycles, as well as due to the influx of novel heat transfer designs presented by the advent of additive manufacturing.