Dataset for boiling acoustic emissions: A tool for data driven boiling regime prediction

Boiling is used for the thermal management of high-energy-density devices and systems. However, sudden thermal runaway at boiling crisis often results in catastrophic failures. Machine learning is a promising tool for in-situ monitoring of boiling-based systems for preemptive control of boiling crisis. A carefully acquired and well-labeled dataset is a primary requirement for utilizing any data-driven learning framework to extract valuable descriptors. Here, we present a comprehensive dataset of boiling acoustics presented in our recent work [1]. We collect the audio files through meticulously controlled near-saturated pool boiling experiments under steady-state conditions. To this end, we connect a high-sensitivity hydrophone to a pre-amplifier and a data acquisition unit for accurate and reliable acquisition of acoustic signals. We organize the audio files into four categories as per the respective boiling regimes: background or natural convection (BKG, 2−5W/cm2), nucleate boiling (NB, 8−140W/cm2), excluding those at higher heat flux values preceding the onset of boiling crisis or the critical heat flux (Pre-CHF, ≈145W/cm2), and transition boiling (TB, uncontrolled). Each audio file label provides explicit information about the heat flux value and the experimental conditions. This dataset, consisting of 2056 files for BKG, 13367 files for NB, 399 files for Pre-CHF, and 460 files for TB, serves as the foundation for training and evaluating a deep learning strategy to predict boiling regimes. The dataset also includes acoustic emission data from transient pool boiling experiments conducted with varying heating strategies, heater surface, and boiling fluid modifications, creating a valuable dataset for developing robust data-driven models to predict boiling regimes. We also provide the associated MATLAB® codes used to process and classify these audio files.