AI Integration in Two-Phase Heat Transfer Research

By Soham Nandi

In an article published in the journal Nature, researchers explored the integration of artificial intelligence (AI) and machine learning (ML) in two-phase heat transfer research, specifically focusing on boiling and condensation phenomena.

AI technologies offer diverse opportunities for scientific advances in phase change heat transfer. In this review, (a) the current challenges of phase-change phenomena are discussed along with (b) AI technologies categorized from an objective-based studies, followed by (c) their outlooks and future perspectives. The tiers are chronologically ordered to reflect the past, present, and future implementation progress of AI within this field. Image Credit: https://www.nature.com/articles/s41524-024-01223-8

They discussed how AI could provide new insights, enabling the collection of physically meaningful features, solving for physical quantities based on first principles, and aiding in meta-analysis, data extraction, and data stream analysis. The authors also considered future perspectives on multidisciplinary collaboration and sustainable cyberinfrastructures.

Background

Phase-change heat transfer, which involves boiling and condensation, is crucial for the development of energy conversion and thermal management systems due to its ability to effectively transfer large amounts of energy. This process is driven by the nucleation of dispersed phases, such as bubbles and droplets, which can be manipulated by adjusting experimental designs to optimize heat transfer performance.

Previous research in phase-change heat transfer has focused on studying the dynamics of bubbles and droplets through the lens of nucleation theory, thermodynamics, and phenomenological correlations. These approaches have improved understanding but have not fully unraveled the mechanistic relationships between experimental factors, nucleation statistics, and thermal performance. Data inconsistency from varying operating conditions and measurement uncertainties, along with the high-dimensional and dynamic nature of boiling and condensation behaviors, have posed significant challenges.

This paper addressed these gaps by leveraging AI and ML technologies to push the boundaries of phase-change heat transfer research. By applying AI to meta-analysis, data extraction, and data stream analysis, the study revealed new insights and offered potential solutions to the difficulties in predicting multi-phase flow patterns. It presented a forward-looking perspective on physics-centered ML and interdisciplinary collaboration for advancing this field.

State-of-the-art AI Technologies for Phase-change Heat Transfers

The authors discussed current AI technologies in the heat transfer community, and categorized them into three main types that target major two-phase heat transfer problems. The types are meta-analysis, physical feature extraction, and data stream analysis.

• Meta-analysis: Meta-analysis in phase-change heat transfer research used ML to provide a holistic view of data by combining and comparing datasets. This approach was crucial as data collection was costly and involved complex experimental factors. ML-assisted meta-analysis developed data-driven hypotheses, designed experiments, and trained neural networks to find relationships between experimental factors and outcomes such as heat transfer performance.

Regression models like feed-forward artificial neural networks (ANNs) and random forest (RF) were popular for meta-analysis, modeling complex nonlinear relationships. Other ML algorithms included support vector machines (SVM), boosting algorithms, and physics-informed neural networks (PINN).

Challenges in training data for meta-analysis included data sparsity and high experimental costs. Solutions such as PINNs and surrogate ML models efficiently integrated measurements and simulations while improving generalizability with limited data. These innovations presented opportunities for phase-change heat transfer research.

• Physical Feature Extraction: Extracting physically meaningful features from visual data could enhance the understanding of two-phase physics and nucleation behaviors during phase-change processes. AI-assisted computer vision (CV) was effective in image analysis, leveraging convolutional neural networks (CNNs) for high accuracy.

Traditional CV algorithms had limitations due to their reliance on handcrafted features and sensitivity to image variations. In contrast, deep learning (DL) models offered flexibility and adaptability, improving analysis across various datasets. DL models excelled in extracting spatiotemporal bubble and droplet statistics for heat transfer analysis in boiling and condensation studies, enabling high-resolution mapping and reduced measurement uncertainty.

Challenges in DL-based feature extraction included two-dimensional representation of three-dimensional phenomena, occlusion, and data preparation. Researchers addressed these challenges through multi-step shape reconstruction, synthetic datasets, and semi-supervised learning.

Efforts continued to improve training methodologies and explore key features describing underlying mechanisms in boiling and condensation. Researchers had opportunities to utilize tracking modules in object detection frameworks to gain higher-order insights from videos. Despite challenges, the effort-to-reward ratio was favorable for advancing our understanding of two-phase phenomena.

• Data-stream Analysis: AI research on phase-change processes focused on analyzing transient data during phase change to understand flow patterns and heat transfer performance. Researchers classified multi-phase flow patterns, such as boiling and condensation, into categories like liquid, slug, and vapor regimes.

Real-time data stream analysis could identify or predict nucleation phases and patterns, benefiting smart systems for adaptive decision-making. ML could classify discrete outputs and forecast continuous outputs using regression analysis. Visual forecasting approaches were computationally expensive but could predict continuous outputs more effectively.

Challenges included improving model transferability across diverse boundary conditions, identifying indicative features, and balancing the processing time with the level of physics extracted. Structured data inputs offered quicker predictions but might lack physics insights, while unstructured data inputs offered deeper insights at a cost of slower processing. New techniques such as neuromorphic event cameras and advanced DL models might offer real-time predictions and forecasting.

Outlook and Future Perspectives

AI-based solutions offered significant potential for the heat transfer community, fostering advancements in two-phase heat and mass transfer performance. By integrating data-driven models with domain expertise, researchers could overcome challenges in quantifying complex multi-phase physics and improve understanding of phase-change processes.

AI models incorporating known physics could lead to accurate and interpretable results, while cyberinfrastructures could provide open-source data, scalable algorithms, and software for long-term sustainability. Collecting diverse datasets across different boundary conditions was crucial for developing data clusters and promoting the sharing of assets within the research community.

Technologies ensuring data privacy and safe sharing, such as encrypted neural networks, were essential for collaborative progress. AI could enhance communication between multiple disciplines, such as materials and thermofluidic sciences, by providing tools for efficient materials design and phase-change heat transfer dynamics.

AI-assisted design tools like matminer had already shown success in material sciences, and similar collaborations could lead to holistic descriptions of heat transfer dynamics. This interdisciplinary approach could lead to next-generation designs for applications like energy conversion devices and two-phase electronics cooling devices, benefitting from AI technologies to optimize thermal management.

Conclusion

In conclusion, the integration of AI and ML in two-phase heat transfer research presented a transformative opportunity for advancing our understanding of boiling and condensation phenomena.

By leveraging AI technologies in meta-analysis, physical feature extraction, and data stream analysis, researchers could overcome challenges in predicting multi-phase flow patterns and enhance thermal management systems. This innovative approach would lead to next-generation designs for applications such as energy conversion devices and two-phase electronics cooling systems, ultimately benefiting from AI-driven advancements in heat transfer dynamics.

Journal reference:

• Suh, Y., Chandramowlishwaran, A., & Won, Y. (2024). Recent progress of artificial intelligence for liquid-vapor phase change heat transfer. Npj Computational Materials, 10(1), 1–14. https://doi.org/10.1038/s41524-024-01223-8, https://www.nature.com/articles/s41524-024-01223-8