Utilization of XGBoost algorithm to predict dryout incipience quality for saturated flow boiling in mini/micro-channels

Critical heat flux (CHF) is arguably the most important design parameter for applications involving cooling of high heat flux devices. Despite prior limited successes, developing accurate predictive tools for CHF remains quite challenging, but key identifiable trigger mechanisms for CHF are intermittent liquid film dryout, complete liquid film dryout, and departure from nucleate boiling (DNB). For saturated flow boiling in mini/micro-channels, dryout incipience is a necessary condition for commencement of liquid film dryout, which is marked by a substantial deterioration in the heat transfer coefficient. The primary objective of this study is to predict system parameters for dryout incipience quality using machine learning, relying on a massive database for this parameter. Predictions are achieved using eXtreme Gradient Boosting (XGBoost), one of the supervised ensemble machine learning methods. This technique is applied to the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) consolidated database for dryout incipience quality for saturated flow boiling in mini/micro-channels. This database comprises 997 datapoints amassed from 26 sources and encompasses 13 different working fluids, hydraulic diameters from 0.51 to 6.0 mm, mass velocities from 29 to 2303 kg/m²s, liquid-only Reynolds numbers from 125 to 53,770, boiling numbers from 0.31×10⁴ to 44.3×10⁴, and reduced pressures from 0.005 to 0.78. Optuna, a supervised automated hyper-parameter optimization software, is used to set the best hyper-parameters in the learning process based on the consolidated database. A part of consolidated database is used to train the XGBoost algorithm, and the XGBoost machine, consisting of specific input parameters, is developed with appropriately determined hyper-parameter set after optimization. The trained XGBoost machine is shown to provide remarkable accuracy in predicting the dryout incipience quality, evidenced by a mean absolute error (MAE) of 2.45% and mean absolute deviation (MAD) of 3.57×10⁻².