Deep learning the sound of boiling for advance prediction of boiling crisis
Kumar Nishant Ranjan Sinha, Vijay Kumar, Nirbhay Kumar, Atul Thakur, Rishi Raj
Abstract
Advance prediction of boiling crisis is critical to the safety and economy of many thermal systems. Here, we perform steady-state near-saturated boiling experiments on a plain copper surface and acquire the acoustic emissions (AEs) in natural convection, nucleate, and transition boiling regimes. We use the corresponding AE spectrograms to train a convolutional neural network, which shows a validation accuracy of 99.92% against the ground truth. We next evaluate the trained network on experiments with water and aqueous solutions of ionic liquid and surfactant on plain and nanostructured copper surfaces with time-varying heat inputs. Despite the variations in boiling surfaces, working fluids, and the heating strategy between the training and the evaluation datasets, the network accurately predicts the respective boiling regimes. Finally, we use the insights to perform advance prediction of boiling crisis for mitigating thermal runaway-induced accidents in boiling-based systems.