Abstract
ABSTRACT This study examines the influence of the thermophysical properties of various partition materials on heat transfer processes in a cavity filled with liquid. To achieve this goal, both direct computational fluid dynamics (CFD) and machine learning (ML) methods were applied. Direct CFD simulations were performed to obtain a detailed understanding of the temperature distribution within the cavity for different septum materials. Subsequently, machine learning methods were applied to create regression models capable of predicting changes in the temperature value inside the cavity based on different septum materials. The best results were achieved using third‐degree polynomial regression, achieving a coefficient of determination ( R 2 ) of 0.816. This indicates that the model is well adapted to the data and is able to explain the variability of the target variable. Additionally, random forest regression models were examined, but their results were slightly less accurate compared to polynomial regression. The study demonstrates that integrating machine learning techniques into computational fluid dynamics can significantly improve the analysis and prediction of thermal processes.