Review: Artificial Intelligence for Liquid-Vapor Phase-Change Heat Transfer

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

BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning

In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multiphysics phenomena. To bridge this gap, we present the BubbleML Dataset \footnote{\label{git_dataset}\url{https://github.com/HPCForge/BubbleML}} which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multiphysics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.Comment: Submitted to Neurips Datasets and Benchmarks Track 202

Characterization of Microscopic Thermofluidic Transport for Development of Porous Media Used in Phase-Change Devices

As the heat generation at the device footprint continuously increases in modern high-tech electronics, there is an urgent need to develop cooling devices that can dissipate highly concentrated heat loads. Phase-change cooling strategies leverage the high latent heat of vaporization of the working fluid to effectively dissipate large heat fluxes. Typical passive two-phase cooling devices utilize the thermal and fluidic transport properties of porous microstructures called the wick that facilitate evaporative heat transfer. Despite the increasing attention that the wick receives for phase-change devices, developing the optimal wick has been a daunting challenge due to the limited resolution of extant thermofluidic characterization techniques. From the designing perspective, it has been difficult to precisely predict how microstructures affect thermofluidic transport through traditional low-resolution experiments. From the manufacturing perspective, large-scale (> 100 m) fabrication of self-assembled wick templates are restricted due to solvent evaporation-induced structural defects called grain boundaries. Motivated by this multistep task, this thesis reports experimental characterization of multiscale thermal and fluidic transport phenomena occurring in porous media using a novel micro laser induced fluorescence (μLIF) technique. Primarily, 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 and 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 decompose key thermofluidic parameters contributing to the wick’s evaporative performance.We then extend this work to study in situ interparticle interactions in colloidal assemblies that decide grain boundaries in colloidal wick templates. The fluorescence microscopy measures the saturation levels with high fidelity to identify distinct colloidal structuring regimes during self-assembly as well as cracking mechanics.The presented studies help decompose the key thermofluidic parameters contributing to the evaporative performance of microscale structures and offer fundamental groundworks for developing large-scale crack-free colloidal wick templates using colloidal self-assembly fabrication

Bridging The Gap: Vision-Inspired Two-Phase Heat Transfer Analysis

Bridging The Gap:Vision-Inspired Two-Phase Heat Transfer AnalysisByYoungjoon Suh Doctor of Philosophy in Mechanical and Aerospace Engineering University of California, Irvine, 2022 Professor Yoonjin Won, ChairAbstractLiquid-vapor phase-change phenomena have been critical to maintaining sustainable and habitable environments on Earth for countless millennia, and are continuing to play central roles in present-day’s industries with ever growing presence. Among different types of phase-change processes, boiling and condensation are two of the most widely used in both domestic and industrial applications. Central to the mechanistic understanding of the thermofluidic processes governing the phase-change phenomena is the rapid and high-fidelity extraction of interpretable physical descriptors from the highly-transient nucleation behaviors. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. This thesis focuses on addressing the fundamentally weak connection between phase-change heat and mass transfer and nucleation statistics available in visual data streams. We outline core ideas of current artificial intelligence (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 physics. The comprehensive review offers insight into the role of recent advances in AI and computer vision in advancing modern boiling and condensation research. Based on foundational literature analysis and problem definition, the remainder of the thesis proposes various AI-based solutions for connecting visual data streams with heat and mass transfer performances at the device and system level. First, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge machine 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. Next, we demonstrate an intelligent vision-based framework called Vision Inspired Online Nuclei Tracker (VISION-iT), which unites classical thermofluidic imaging techniques with deep learning to fundamentally address the challenge of extracting high-fidelity interpretable physical descriptors for the highly-transient two-phase processes. We introduce and discuss the detailed construction, algorithms, and optimization guidelines of individual modules so that the framework can easily be adjusted to custom datasets. The concepts and procedures that we propose is transferable, and thus can benefit a broader audience dealing with similar problems. Finally, VISION-iT is deployed in practical phase-change heat transfer analysis applications. For boiling applications, 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. For condensation applications, the data-centric analysis enabled by VISION-iT conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key trade-off between heat transfer rate per individual droplet and droplet population density. Our 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

Bridging The Gap: Vision-Inspired Two-Phase Heat Transfer Analysis

Bridging The Gap:Vision-Inspired Two-Phase Heat Transfer AnalysisByYoungjoon Suh Doctor of Philosophy in Mechanical and Aerospace Engineering University of California, Irvine, 2022 Professor Yoonjin Won, ChairAbstractLiquid-vapor phase-change phenomena have been critical to maintaining sustainable and habitable environments on Earth for countless millennia, and are continuing to play central roles in present-day’s industries with ever growing presence. Among different types of phase-change processes, boiling and condensation are two of the most widely used in both domestic and industrial applications. Central to the mechanistic understanding of the thermofluidic processes governing the phase-change phenomena is the rapid and high-fidelity extraction of interpretable physical descriptors from the highly-transient nucleation behaviors. However, extracting quantifiable measures out of dynamic objects with conventional imaging technologies poses a challenge to researchers. This thesis focuses on addressing the fundamentally weak connection between phase-change heat and mass transfer and nucleation statistics available in visual data streams. We outline core ideas of current artificial intelligence (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 physics. The comprehensive review offers insight into the role of recent advances in AI and computer vision in advancing modern boiling and condensation research. Based on foundational literature analysis and problem definition, the remainder of the thesis proposes various AI-based solutions for connecting visual data streams with heat and mass transfer performances at the device and system level. First, we introduce a data-driven learning framework that correlates high-quality imaging on dynamic bubbles with associated boiling curves. The framework leverages cutting-edge machine 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. Next, we demonstrate an intelligent vision-based framework called Vision Inspired Online Nuclei Tracker (VISION-iT), which unites classical thermofluidic imaging techniques with deep learning to fundamentally address the challenge of extracting high-fidelity interpretable physical descriptors for the highly-transient two-phase processes. We introduce and discuss the detailed construction, algorithms, and optimization guidelines of individual modules so that the framework can easily be adjusted to custom datasets. The concepts and procedures that we propose is transferable, and thus can benefit a broader audience dealing with similar problems. Finally, VISION-iT is deployed in practical phase-change heat transfer analysis applications. For boiling applications, 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. For condensation applications, the data-centric analysis enabled by VISION-iT conclusively shows that contrary to classical understanding, the overall condensation performance is governed by a key trade-off between heat transfer rate per individual droplet and droplet population density. Our 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

Deep learning predicts boiling heat transfer.

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

Building a Knowledge Brokering System using social network analysis: A case study of the Korean financial industry

The importance of knowledge is increasing in our global and knowledge-based society. As a part of knowledge management, successful knowledge transfer can improve an organization's competitive advantages and increase an organization's valuable knowledge assets. However, knowledge transfer is complex and a lot of factors exist that affect successful knowledge transfer such as context, social networks, and IT/IS. This paper aims at the role of the knowledge broker which is to be a link between knowledge seekers and knowledge experts. In this context, this research implemented a Knowledge Brokering System - called K-broker system - as a prototype system to improve knowledge transfer in an organization based on an analysis of users' social network. The K-broker system can provide a 'single view' screen for identifying knowledge experts and has no bottlenecks in contrast with a human knowledge broker and can provide a permanent communication channel between knowledge seekers and knowledge experts. (C) 2011 Elsevier Ltd. All rights reserved.X11813sciescopu