The present study applied image-trained Convolutional Neural Networks (CNN) to quantify the heat flux dissipated in a pool boiling experiment. In total, four heating surfaces were used to generate over 200 thousand nucleate and film boiling images in two visualization modes: direct and indirect. In the former, the flow images included the heating surface; in the latter, the image was cropped and the heating surface was omitted. Prior to training and testing the CNN, the images were preprocessed, by grayscaling, downscaling, size uniformization and standardization. The main goals were to assess the CNN's capability to generalize to multiple operating conditions, and to quantify the performance of a CNN architecture optimized with Automated Machine Learning (AutoML) in comparison with a reference architecture. The results suggest that multi-dataset training is required to improve the CNN generalization. In other words, the CNN must be trained, even if partially, with images of the specific heating surface for which it is trying to infer the heat flux. However, the results indicate a significant performance drop for the results of multi-surface trained CNNs when compared with single-surface CNNs, suggesting limited generalization capability. Furthermore, the results obtained showed that AutoML was capable of increasing the performance of CNN models when compared with parametrically determined architectures. Also, optimized architectures tend to present a larger number of convolutional layers associated with dense blocks and, at the same time, a reduced number of trainable variables.
