Deep Learning Strategies for Critical Heat Flux Detection in Pool Boiling

Image-based deep learning (DL) models are employed to enable the detection of critical heat flux (CHF) based on pool boiling experimental images. Most machine learning approaches for pool boiling to date focus on a single dataset under a certain heater surface, working fluid, and operating conditions. For new datasets collected under different conditions, a significant effort in re-training the model or developing a new model is required under the assumption that the new dataset has a sufficient amount of labeled data. This research is to explore supervised, semi-supervised, and unsupervised machine learning strategies that are formulated to adapt to two scenarios. The first is when the new dataset has limited labeled data available. This scenario was addressed in chapter 2 of this thesis, where Convolutional Neural Networks (CNNs) and Transfer learning (TL) were used in tackling such situations. The second scenario is when the new dataset has no labeled data available at all. In such cases, this research presents a methodology in Chapter 3, where one of the state-of-the-art Generative Adversarial Networks (GANs) called Fixed-Point GAN is deployed in collaboration with a regular CNN model to tackle the problem. To the best of my knowledge, the approaches presented in chapters 2 and 3 are the first of their kind to utilize TL and GANs to solve the boiling heat transfer problem within the heat transfer community and are a step forward towards obtaining a one-for-all general model.