An Optimized Stacking Ensemble Learning Model Using 3-Pyramids Technique for the 2006 CHF Groeneveld Look Table Prediction

Typically, critical heat flux (CHF) look-up tables constructed based on physical experiments have limited data. Using available experimental-based CHF look-up tables as training data, machine learning techniques can be applied to construct CHF prediction models and, thus, produce CHF look-up tables reporting estimated CHF values under a much wider variety of conditions. This study proposes an effective prediction approach based on stacking ensemble learning and a new optimization technique, namely 3-pyramids, to get reliable CHF look-up prediction results. The approach is divided into three parts that include (1) using multiple prediction models constructed using different machine learning techniques, (2) combining the prediction results using a stacking strategy to generate the final prediction results, and (3) optimizing an improved super learner model to improve the prediction model's capabilities using 3-pyramids optimization technique. The effectiveness of these models was evaluated using the coefficient of determination (R
²
), mean absolute error (MAE), and root-mean-squared (RMSE). The results show that the proposed model has high accuracy and that applying the 3-pyramids optimization further improved this accuracy and significantly reduced the model execution time.