Micro- and nanostructured surfaces offer the potential to enhance two-phase heat transfer. However, the mechanisms behind these enhancements are not well-understood due to insufficient diagnostic methods, leading to reliance on trial-and-error surface development. We introduce in situ boroscopy to investigate microscale bubble dynamics during flow boiling nucleation and subsequent flow regime development. This method was applied in saturated flow boiling experiments within chemically etched aluminum and copper tubes. Although the surfaces have self-similar surface structures, our findings revealed varied heat transfer coefficient enhancements, with increases of up to 391% on aluminum and 41% on copper. Using boroscopy, we identified key mechanisms of heat transfer enhancement. We further used mercury porosimetry to determine the impact of pore size distribution on thermal performance. The boroscopy technique introduced here not only elucidates the underlying processes of flow boiling heat transfer enhancement but also has potential applications for the study of other two-phase phenomena.

Surfaces with micrometer-scale pillars have shown great potential in delaying the boiling crisis and enhancing the critical heat flux (CHF). However, physical mechanisms enabling this enhancement remain unclear. This knowledge gap is due to a lack of diagnostics that allow elucidating how micro-pillars affect thermal transport phenomena on the engineered surface. In this study, for the first time, we are able to measure time-dependent temperature and heat flux distributions on a boiling surface with engineered micro-pillars using infrared thermometry. Using these data, we reveal the presence of an intra-pillar liquid layer, created by the nucleation of bubbles and partially refilled by capillary effects. However, contrarily to conventional wisdom, the energy removed by the evaporation of this liquid cannot explain the observed CHF enhancement. Yet, predicting its dry out is the key to delaying the boiling crisis. We achieve this goal using simple analytic models and demonstrate that this process is driven by conduction effects in the boiling substrates and, importantly, in the intra-pillar liquid layer itself. Importantly, these effects also control the wicking flow rate and its penetration length. The boiling crisis occurs when, by coalescing, the size of the intra-pillar liquid layer becomes too large for the wicking flow to reach its innermost region. Our study reveals and quantifies unidentified physical aspects, key to the performance optimization of boiling surfaces for cooling applications.

Enhancing the flow boiling critical heat flux (CHF) is beneficial to the economics and safety margins of many industrial applications cooled by boiling heat transfer. While many studies have shown that surfaces with hydrophilic nanoscale and micro-scale features can enhance CHF in pool boiling, it is still not clear how these engineered surfaces affect the CHF in subcooled flow boiling at ambient pressure, let alone high-pressure conditions. Here, two nano-engineered surfaces, i.e., a surface coated with a porous layer of hydrophilic silica nanoparticles and a surface coated with zinc oxide nanowires, were tested. Flow boiling tests with a 10 K subcooling and a mass flux of 1000 kg/(m²·s) were conducted at 1 bar and 4 bars using infrared thermometry diagnostics. At 1 bar, the CHF enhancement is around 15% for both coatings. At 4 bars, the CHF enhancement is around 17% for the nanowire surface, and around 25% for the nano-porous surface. Infrared thermometry measurements reveal that the CHF enhancement comes from an increase of both two-phase heat transfer and single-phase heat transfer mechanisms, which is due to a change of bubble dynamics on the nanoengineered surfaces. It is also shown that the boiling crisis can be predicted using a percolation model based on Monte-Carlo simulations.

Infrared thermography is a powerful tool for studying liquid-to-vapor phase change processes. However, its application has been limited in the study of vapor-to-liquid phase transitions due to the presence of complex liquid dynamics, multiple phases within the same field of view, and experimental difficulty. Here, we develop a calibration framework which is capable to studying one of the most complex two-phase heat transfer processes: dropwise condensation. The framework accounts for non-uniformities arising from dynamic two-phase interactions such as droplet nucleation, growth, coalescence, and departure, as well as substrate effects particularly observed on micro- and nanoengineered surfaces. This approach enables high-resolution temperature measurements with both spatial (12 $μ$m) and temporal (5 ms) precision, leading to the discovery of local temperature phenomena unobservable using conventional approaches. These observed temperature variations are linked to droplet statistics, showing how different regions contribute to local condensation heat transfer. We extend the developed method to quantify local thermal parameters by fusing it with a generative machine learning model to map visual images into temperature fields. The model is informed of the physical parameter by incorporating vapor pressure embedding as the conditional parameter. This work represents a significant step toward simplifying local temperature measurements for vapor-to-liquid phase change phenomena by developing a methodology as well as a machine learning approach to map local thermal phenomena using only optical images as the input.