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.

Leidenfrost phenomenon during quenching in aqueous solutions: effect of evaporation-induced concentration gradients.

The minimum temperature limit for a sustained vapor film on a hot surface defines the well-known Leidenfrost temperature (LFT). LFT for pure fluids is typically a strong function of the surface tension. However, the effect of surface tension on LFT of aqueous additive solutions is confusing with many complicated trends. For example, despite an insignificant increase of ≈1 mN m-1 in surface tension, a substantial increase in LFT of ≈50 °C with aqueous salt and sugar solutions has been reported in comparison to pure water. Conversely, no appreciable change in LFT (within ±2 °C) is observed despite a substantial drop of up to ≈30 mN m-1 in surface tension upon varying the concentration of surfactant additives in aqueous solutions. Here, we perform simultaneous thermal, visual, and acoustic characterization of pool quenching experiments with aqueous solutions of salt, sugar, surfactant, and ionic liquids. We model the evaporation-induced increase in the concentration of the non-volatile additives at the liquid-vapor interface using Fick's second law of diffusion. We show that the localized concentration buildup of additives at the liquid-vapor interface dramatically alters the surface tension values in comparison to the typical equilibrium values estimated otherwise. We use these modified surface tension values to correlate the diverse set of experimental LFT data reported in our work and in the literature using a unified framework. We believe that these clarifications regarding the Leidenfrost mechanism will encourage the use of additives in various applications, specifically those where surface modification strategies may not be practically feasible.