Machine Learning for Fluid-Agnostic Laminar Heat Transfer Predictions Under Supercritical Conditions

Machine learning was employed to make fluid agnostic laminar heat transfer prediction in supercritical conditions, encompassing three fluids (sCO2, sH2O, sC10H22) representing a wide range of operating conditions. High-fidelity training data, consisting of both non-dimensional and dimensional (operating parameter) as inputs and Nu and Twall as outputs, were generated from grid-converged, steady-state, computational fluid dynamic (CFD) simulations. The Random Forest (RF) algorithm outperformed the artificial neural networks (ANNs) across all scenarios on the small multi-fluid dataset (~1600 data points) employed during the training process. When using non-dimensional parameters as inputs, Nu prediction fidelities were better than Twall predictions for both ML algorithms across both horizontal and vertical configurations. The RF model trained on data from a specific flow configuration (horizontal/vertical) could predict Twall within an accuracy of +/−1% with dimensional, operational parameters as inputs while being agnostic to the working fluid. Furthermore, by including the gravity vector as an additional variable during the training process, the RF model could predict Twall accurately in a mixed, multi-fluid dataset containing data from both horizontal and vertical configurations.