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相关概念视频

Mechanism of heat transfer01:19

Mechanism of heat transfer

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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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Quantifying Heat02:46

Quantifying Heat

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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a...
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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...
3.2K
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

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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Heat Engines01:10

Heat Engines

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A heat engine is a device used to extract heat from a source and then convert it into mechanical work used for various applications. For example, a steam engine on an old-style train can produce the work needed for driving the train.
Whenever we consider heat engines (and associated devices such as refrigerators and heat pumps), we do not use the standard sign convention for heat and work. For convenience, we assume that the symbols Qh, Qc, and W represent only the amounts of heat transferred...
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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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基于等效热源方法和重建变量自编码器的多热源布局优化研究.

Yide Yang1, Mali Gong2, Jianshe Ma3

  • 1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.

Scientific reports
|September 11, 2024
PubMed
概括

本研究引入了一种用于预测热管理的新型RFCNN-βVAE模型. 深度学习方法准确模拟温度上升,优化热源的位置,优于基准标准.

关键词:
深度学习是一种深度学习.相当的热源方法方法.布局优化 布局优化这就是VAE的意义.

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相关实验视频

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科学领域:

  • 热管理 热管理
  • 计算流体动力学的流体动力学.
  • 人工智能的人工智能

背景情况:

  • 像变量自编码器 (VAE) 这样的预测图像生成模型显示出希望.
  • 在热系统中处理多个热源装置存在挑战.
  • 对于联对流传热传输的传统数值解决方案可能是计算密集的.

研究的目的:

  • 提出并验证一个新的RFCNN-βVAE模型,用于准确的温度上升模拟和热管理.
  • 开发一种有效的深度学习方法来优化多热源配置.
  • 为2D联对流传热传输问题提供传统数值方法的可靠替代方案.

主要方法:

  • 开发了一个剩余连接的完全连接的神经网络与变量自编码器 (RFCNN-βVAE) 模型.
  • 综合分析解决方案,多项式拟合和温度场叠加用于模拟.
  • 利用多个等效热源来建模亚亚巴特的边界条件.
  • 调整了β参数以平衡重建准确度和潜空间泛化.

主要成果:

  • 该模型在模拟温度上升分布方面实现了高预测精度.
  • RFCNN-βVAE模型成功优化了热源坐标,最大限度地降低了峰值温度上升.
  • 在优化过程中,73.4%的结果超过了密集数据集基准.
  • 证明了深度学习在热管理系统设计中的有效性.

结论:

  • 拟议的RFCNN-βVAE模型为热管理提供了可行和有效的深度学习解决方案.
  • 这项研究为优化具有多个热源的复杂热环境提供了宝贵的见解.
  • 这项研究验证了AI在设计高效的热系统中的应用,例如多束照明均等.