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Abstract Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past, for making their insights available to other domains, and for solving for physical quantities based on first principles for phase-change thermofluidic systems. This review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation phenomena. AI technologies for meta-analysis, data extraction, and data stream analysis are described with their potential challenges, opportunities, and alternative approaches. Finally, we offer outlooks and perspectives regarding physics-centered machine learning, sustainable cyberinfrastructures, and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer. 

The boiling efficacy is intrinsically tethered to trade-offs between the desire for bubble nucleation and necessity of vapor removal. The solution to these competing demands requires the separation of bubble activity and liquid delivery, often achieved through surface engineering. In this study, we independently engineer bubble nucleation and departure mechanisms through the design of heterogeneous and segmented nanowires with dual wettability with the aim of pushing the limit of structure-enhanced boiling heat transfer performances. The demonstration of separating liquid and vapor pathways outperforms state-of-the-art hierarchical nanowires, in particular, at low heat flux regimes while maintaining equal performances at high heat fluxes. A deep-learning based computer vision framework realized the autonomous curation and extraction of hidden big data along with digitalized bubbles. The combined efforts of materials design, deep learning techniques, and data-driven approach shed light on the mechanistic relationship between vapor/liquid pathways, bubble statistics, and phase change performance. 

Abstract Boiling is arguably Nature’s most effective thermal management mechanism that cools submersed matter through bubble-induced advective transport. Central to the boiling process is the development of bubbles. Connecting boiling physics with bubble dynamics is an important, yet daunting challenge because of the intrinsically complex and high dimensional of bubble dynamics. Here, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge deep learning models including convolutional neural networks and object detection algorithms to automatically extract both hierarchical and physics-based features. By training on these features, our model learns physical boiling laws that statistically describe the manner in which bubbles nucleate, coalesce, and depart under boiling conditions, enabling in situ boiling curve prediction with a mean error of 6%. Our framework offers an automated, learning-based, alternative to conventional boiling heat transfer metrology. 

Abstract As the heat generation at device footprint continuously increases in modern high-tech electronics, there is an urgent need to develop new cooling devices that balance the increasing power demands. To meet this need, cutting-edge cooling devices often utilize microscale structures that facilitate two-phase heat transfer. However, it has been difficult to understand how microstructures enhance evaporation performances through traditional experimental methods due to low spatial resolution. The previous methods can only provide coarse interpretations on how physical properties such as permeability, thermal conduction, and effective surface areas interact at the microscale to effectively dissipate heat. This motivates researchers to develop new methods to observe and analyze local evaporation phenomena at the microscale. Herein, we present techniques to characterize submicron to macroscale evaporative phenomena of microscale structures by using microlaser-induced fluorescence (μLIF). We corroborate the use of unsealed temperature-sensitive dyes by systematically investigating the effects of temperature, concentration, and liquid thickness on the fluorescence intensity. Considering these factors, we analyze the evaporative performances of microstructures using two approaches. The first approach characterizes the overall and local evaporation rates by measuring the solution drying time. The second approach employs an intensity-to-temperature calibration curve to convert temperature-sensitive fluorescence signals to surface temperatures, which calculates the submicron-level evaporation rates. Using these methods, we reveal that the local evaporation rate between microstructures is high but is balanced with a large capillary-feeding. This study will enable engineers to decompose the key thermofluidic parameters contributing to the evaporative performance of microscale structures. 

Abstract As modern electronics continuously exceed their performance limits, there is an urgent need to develop new cooling devices that balance the increasing power demands. To meet this need, cutting-edge cooling devices often utilize microscale structures that facilitate two-phase heat transfer. However, it has been difficult to understand how microstructures trigger enhanced evaporation performances through traditional experimental methods due to low spatial resolution. The previous methods can only provide coarse interpretations on how physical properties such as permeability, thermal conduction, and effective surface areas interact at the microscale to effectively dissipate heat. This motivates researchers to develop new methods to observe and analyze local evaporation phenomena at the microscale. Herein, we present techniques to characterize submicron to macroscale evaporative phenomena of microscale structures using micro laser induced fluorescence (μLIF). We corroborate the use of unsealed temperature-sensitive dyes by systematically investigating their effects on temperature, concentration, and liquid thickness on the fluorescence intensity. Considering these factors, we analyze the evaporative performances of microstructures using two approaches. The first approach characterizes local or overall evaporation rates by measuring the solution drying time. The second method employs an intensity-to-temperature calibration curve to convert temperature-sensitive fluorescence signals to surface temperatures. Then, submicron-level evaporation rates are calculated by employing a species transport equation for vapor at the liquid-vapor interface. Using these methods, we reveal that capillary-assisted liquid feeding dominates evaporation phenomena on microstructured surfaces. This study will enable engineers to decompose the key thermofluidic parameters contributing to the evaporative performance of microscale structures. 

Abstract Condensation is ubiquitous in nature and industry. Heterogeneous condensation on surfaces is typified by the continuous cycle of droplet nucleation, growth, and departure. Central to the mechanistic understanding of the thermofluidic processes governing condensation is the rapid and high‐fidelity extraction of interpretable physical descriptors from the highly transient droplet population. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. Here, an intelligent vision‐based framework is demonstrated that unites classical thermofluidic imaging techniques with deep learning to fundamentally address this challenge. The deep learning framework can autonomously harness physical descriptors and quantify thermal performance at extreme spatio‐temporal resolutions of 300 nm and 200 ms, respectively. The data‐centric analysis conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key tradeoff between heat transfer rate per individual droplet and droplet population density. The vision‐based approach presents a powerful tool for the study of not only phase‐change processes but also any nucleation‐based process within and beyond the thermal science community through the harnessing of big data.