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Related Concept Videos

Mechanisms of Heat Transfer01:14

Mechanisms of Heat Transfer

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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 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 Transfer II01:20

Mechanisms of Heat Transfer II

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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...
3.3K
Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

1.2K
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
1.2K
Mechanism of heat transfer01:19

Mechanism of heat transfer

1.3K
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...
1.3K
Thermal Sigmatropic Reactions: Overview01:16

Thermal Sigmatropic Reactions: Overview

2.1K
Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in...
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Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization.

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Machine learning (ML) accelerates thermal science research by analyzing complex data for materials discovery and system design. This overview explores ML applications in thermal transport, materials, properties, and engineering, highlighting future opportunities.

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Area of Science:

  • Thermal Science and Engineering
  • Materials Science
  • Computational Science

Background:

  • Machine learning (ML) offers novel statistical approaches to analyze complex data.
  • Conventional methods face limitations in uncovering intricate patterns in thermal science.
  • The field of ML is rapidly advancing, presenting new opportunities for scientific discovery.

Purpose of the Study:

  • To provide a comprehensive overview of ML applications in thermal energy research.
  • To explore the potential of ML in materials discovery and system design.
  • To discuss current challenges and future directions for ML in thermal science.

Main Methods:

  • Review of ML applications across various scales, from atomistic to multi-scale.
  • Focus on ML in thermal transport modeling (DFT, MD, BTE).
  • Examination of ML for diverse material families and thermal properties.

Main Results:

  • ML is being applied to semiconductors, polymers, alloys, and composites.
  • ML aids in predicting thermal conductivity, emissivity, stability, and thermoelectricity.
  • ML shows promise in engineering prediction and optimization of thermal devices and systems.

Conclusions:

  • ML presents significant opportunities for advancing thermal energy research.
  • Addressing current challenges and developing new algorithms will be crucial for future impact.
  • ML integration can lead to non-intuitive discoveries and improved thermal management.