skip to main content


Title: Recent progress of artificial intelligence for liquid-vapor phase change heat transfer
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.

 
more » « less
Award ID(s):
2045322
NSF-PAR ID:
10497759
Author(s) / Creator(s):
; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
npj Computational Materials
Volume:
10
Issue:
1
ISSN:
2057-3960
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Heat fluxes in the data center have been increasing significantly due to the rise in advanced technologies such as Artificial Intelligence (AI), 5G, high-performance computing (HPC), and machine learning. The traditional air-cooling technology cannot handle high heat fluxes and requires a bigger heat sink; therefore, hindering high heat flux and high density in the data center. Two-phase cooling schemes are particularly appropriate for high heat flux situations because of their enhanced heat transfer coefficients and the non-linear relationship between heat flux and surface-to-fluid temperature difference. In this study, an experimental setup was developed to characterize and optimize the thermo-hydraulic performance of two-phase cooling cold plates intended for high heat flux applications. An improvement of 12% in thermal performance was obtained by cutting the original fins and creating mini-channels perpendicular to the original microchannels without a significant pressure drop penalty. 
    more » « less
  2. Increasing power densities in data centers due to the rise of Artificial Intelligence (AI), high-performance computing (HPC) and machine learning compel engineers to develop new cooling strategies and designs for high-density data centers. Two-phase cooling is one of the promising technologies which exploits the latent heat of the fluid. This technology is much more effective in removing high heat fluxes than when using the sensible heat of fluid and requires lower coolant flow rates. The latent heat also implies more uniformity in the temperature of a heated surface. Despite the benefits of two-phase cooling, the phase change adds complexities to a system when multiple evaporators (exposed to different heat fluxes potentially) are connected to one coolant distribution unit (CDU). In this paper, a commercial pumped two-phase cooling system is investigated in a rack level. Seventeen 2-rack unit (RU) servers from two distinct models are retrofitted and deployed in the rack. The flow rate and pressure distribution across the rack are studied in various filling ratios. Also, investigated is the transient behavior of the cooling system due to a step change in the information technology (IT) load. 
    more » « less
  3. Chen, Guohua ; Khan, Faisal (Ed.)
    Artificial intelligence (AI) and machine learning (ML) are novel techniques to detect hidden patterns in environmental data. Despite their capabilities, these novel technologies have not been seriously used for real-world problems, such as real-time environmental monitoring. This survey established a framework to advance the novel applications of AI and ML techniques such as Tiny Machine Learning (TinyML) in water environments. The survey covered deep learning models and their advantages over classical ML models. The deep learning algorithms are the heart of TinyML models and are of paramount importance for practical uses in water environments. This survey highlighted the capabilities and discussed the possible applications of the TinyML models in water environments. This study indicated that the TinyML models on microcontrollers are useful for a number of cutting-edge problems in water environments, especially for monitoring purposes. The TinyML models on microcontrollers allow for in situ real-time environmental monitoring without transferring data to the cloud. It is concluded that monitoring systems based on TinyML models offer cheap tools to autonomously track pollutants in water and can replace traditional monitoring methods. 
    more » « less
  4. This paper considers the cultivation of ethical identities among future engineers and computer scientists, particularly those whose professional practice will extensively intersect with emerging technologies enabled by artificial intelligence (AI). Many current engineering and computer science students will go on to participate in the development and refinement of AI, machine learning, robotics, and related technologies, thereby helping to shape the future directions of these applications. Researchers have demonstrated the actual and potential deleterious effects that these technologies can have on individuals and communities. Together, these trends present a timely opportunity to steer AI and robotic design in directions that confront, or at least do not extend, patterns of discrimination, marginalization, and exclusion. Examining ethics interventions in AI and robotics education may yield insights into challenges and opportunities for cultivating ethical engineers. We present our ongoing research on engineering ethics education, examine how our work is situated with respect to current AI and robotics applications, and discuss a curricular module in “Robot Ethics” that was designed to achieve interdisciplinary learning objectives. Finally, we offer recommendations for more effective engineering ethics education, with a specific focus on emerging technologies. 
    more » « less
  5. Abstract Practitioner notes

    What is already known about this topic

    Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

    While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

    There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

    What this paper adds

    Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

    Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

    Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

    Implications for practice and/or policy

    It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

    Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

    To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

     
    more » « less