Multimodal boiling dataset with synchronized acoustic, optical, and thermal measurements under steady-state and transient heat loads.

ABSTRACT

Boiling is a high-performance heat dissipation process that is central to electronics cooling and power generation. However, there exists a practical limit of boiling heat transfer known as the critical heat flux (CHF), beyond which significant performance degradation is observed. Understanding the physical mechanism that triggers CHF is essential to meet the increasing cooling demands driven by power densification and device miniaturization. However, the high dimensionality, stochasticity, and dynamicity of the boiling process have led to strong challenges in the experimental characterization and modeling of boiling CHF. As such, high-frame rate, high-resolution, multi-physics boiling datasets are critical to advancing the fundamental understanding of boiling heat transfer. To this end, this paper presents a multimodal boiling dataset consisting of synchronized thermal, acoustic, and optical signals collected from five different heater surfaces under two distinct heat load conditions. With its high sampling frequency, diverse signal types, large data volume, and detailed recorded information, this dataset provides valuable "data blood" for the field of thermal crisis monitoring. This dataset will not only promote fundamental research on bubble dynamics during boiling but also support the implementation of advanced monitoring technologies in industrial applications such as power electronics, motors, data centers, and power plants.