Visualization-based nucleate boiling heat flux quantification using machine learning

Processes involving complex phenomena are ubiquitous in nature and industry, many of which are difficult to simulate computationally. Nucleate boiling heat transfer, for instance, has numerous practical applications, while the film boiling is an undesirable operation regime. So far, most correlations and computer simulations to quantify boiling heat transfer rely on direct measurement of thermohydraulic data, such as heater temperature, which is often invasive. Here it is demonstrated that neural network-based models can quantify heat transfer using only direct and indirect visual information of the boiling phenomenon, without any prior knowledge of the governing equations, which enables the non-intrusive measurement of heat flux based on boiling process imaging. It is shown that neural networks can encode bubble morphology and its correlation with heat flux returning errors as low as 7% when compared with precise experimental measurements, a significant improvement over current prediction methods of boiling heat transfer. Furthermore, it is shown that these systems may be implemented in inexpensive, compact computers, such as the Raspberry Pi, to infer heat flux in real time from visualization. (C) 2019 Elsevier Ltd. All rights reserved.