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Related Concept Videos

Mechanisms of Heat Transfer I01:14

Mechanisms of Heat Transfer I

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Just as interesting as the effects of heat transfer on a system are the methods by which the heat transfer occur. Whenever there is a temperature difference, heat transfer occurs. It may occur rapidly, such as through a cooking pan, or slowly, such as through the walls of a picnic ice box. So many processes involve heat transfer that it is hard to imagine a situation where no heat transfer occurs. Yet, every heat transfer takes place by only three methods: conduction, convection, and radiation.
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Mechanisms of Heat Transfer01:14

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Heat transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
Conduction, accounting for approximately 3% of body heat loss at rest, is the process of exchanging heat between molecules of two materials in direct contact. This can result in both heat loss and gain. For instance, when the body is submerged in water, which conducts heat 20 times more effectively than air, it can either lose or gain significant...
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Mechanisms of Heat Transfer II01:20

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In convection, thermal energy is carried by the large-scale flow of matter. Ocean currents and large-scale atmospheric circulation, which result from the buoyancy of warm air and water, transfer hot air from the tropics toward the poles and cold air from the poles toward the tropics. The Earth’s rotation interacts with those flows, causing the observed eastward flow of air in the temperate zones. Convection dominates heat transfer by air, and the amount of available space for the airflow...
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Heat is a type of energy transfer that is caused by a temperature difference, and it can change the temperature of an object. Since heat is a form of energy, its SI unit is the joule (J). Another common unit of energy often used for heat is the calorie (cal), which is defined as the energy needed to change the temperature of 1 g of water by 1 °C, specifically between 14.5 °C and 15.5 °C, since the energy needed shows a slight temperature dependence. Another commonly used unit is...
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Mechanism of heat transfer01:19

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Understanding heat transfer mechanisms is essential for understanding how our bodies maintain balance in different environmental conditions. When the environment is thermoneutral, the body is in a state of balance, neither using nor releasing energy to maintain its core temperature. However, when the environment is not thermoneutral, the body employs four heat transfer mechanisms to maintain homeostasis: conduction, convection, evaporation, and radiation. These mechanisms facilitate heat...
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When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
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Updated: Nov 14, 2025

Pool-Boiling Heat-Transfer Enhancement on Cylindrical Surfaces with Hybrid Wettable Patterns
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Deep learning predicts boiling heat transfer.

Youngjoon Suh1, Ramin Bostanabad1, Yoonjin Won2,3

  • 1Department of Mechanical and Aerospace Engineering, University of California, 5200 Engineering Hall, Irvine, CA, 92617-2700, USA.

Scientific Reports
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Summary

This study introduces a deep learning framework to link bubble dynamics with boiling heat transfer. The AI model accurately predicts boiling curves by analyzing bubble behavior, offering a novel metrology approach.

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Area of Science:

  • Thermodynamics
  • Fluid Dynamics
  • Artificial Intelligence

Background:

  • Boiling is a critical heat transfer mechanism driven by bubble dynamics.
  • Understanding the complex relationship between bubble behavior and boiling physics is challenging.
  • Conventional methods for measuring boiling heat transfer are often manual and time-consuming.

Purpose of the Study:

  • To develop a data-driven framework correlating bubble dynamics with boiling curves.
  • To leverage deep learning for automated feature extraction from bubble imaging.
  • To enable accurate in situ prediction of boiling heat transfer.

Main Methods:

  • Utilized high-quality imaging of dynamic bubbles during boiling.
  • Employed deep learning models, including convolutional neural networks and object detection algorithms.
  • Extracted hierarchical and physics-based features from bubble images.
  • Trained a model to learn physical boiling laws from extracted features.

Main Results:

  • The framework successfully correlated bubble dynamics with boiling curves.
  • Achieved in situ boiling curve prediction with a mean error of 6%.
  • Demonstrated automated extraction of relevant bubble features.

Conclusions:

  • The data-driven learning framework provides an effective method for analyzing boiling phenomena.
  • This approach offers an automated, AI-powered alternative to traditional boiling heat transfer metrology.
  • The study advances the understanding of boiling physics through advanced computational techniques.