ArticleScientific reports2021
Deep learning predicts boiling heat transfer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Machine learning-based heat flux estimation from high-speed video during saturated pool boiling over vertical tube.Scientific reports · 2026Article
- Analysis of Multiscale Condensation Phenomena Using a Zero-Shot Computer Vision Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling.Sensors (Basel, Switzerland) · 2024Article
- Deep learning-based inverse design of microstructured materials for optical optimization and thermal radiation control.Scientific reports · 2023Article
- Advances in micro and nanoengineered surfaces for enhancing boiling and condensation heat transfer: a review.Nanoscale advances · 2023Review
- Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network.International journal of molecular sciences · 2023Article
- A Deep Learning Perspective on Dropwise Condensation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.