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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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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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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 transfer between the human body and its environment occurs through four main mechanisms: conduction, convection, radiation, and evaporation.
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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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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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On the Computational Study of a Fully Wetted Longitudinal Porous Heat Exchanger Using a Machine Learning Approach.

Hosam Alhakami1, Naveed Ahmad Khan2, Muhammad Sulaiman2

  • 1Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia.

Entropy (Basel, Switzerland)
|September 23, 2022
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Summary
This summary is machine-generated.

This study models heat transfer in porous fins using artificial neural networks (ANN) and the Tiki-Taka algorithm (TTA). Increased wet porosity enhances heat transfer, while higher power index reduces it, aiding electronic cooling design.

Keywords:
functionally graded materialsmachine learning techniquesmeta-heuristicsthermal analysiswet porous fin

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Area of Science:

  • Heat Transfer
  • Computational Fluid Dynamics
  • Materials Science

Background:

  • Porous fins are crucial in engineering applications for thermal management.
  • Modeling heat transfer in porous media requires addressing complex non-linear phenomena.
  • Understanding thermal behavior is key for optimizing fin design in various environments.

Purpose of the Study:

  • To develop a computational model for the thermal behavior of porous longitudinal fins.
  • To investigate the influence of varying thermal conductivities (linear, quadratic, exponential) and environmental conditions (convective, conductive, radiative).
  • To assess the impact of physical parameters on fin thermal performance.

Main Methods:

  • Formulation of the governing non-linear singular differential equation using the Darcy model.
  • Application of multilayer perceptron artificial neural networks (ANN) for approximate solutions.
  • Optimization using the Tiki-Taka algorithm (TTA) combined with sequential quadratic programming (SQP).

Main Results:

  • The ANN-TTA-SQP algorithm provided accurate and stable solutions with low errors (10-4 to 10-5 absolute error, 10-8 to 10-10 mean square error).
  • Heat transfer rate increases with the wet porosity parameter.
  • Heat transfer rate decreases with an increase in the power index.

Conclusions:

  • The proposed ANN-TTA-SQP method is a reliable tool for analyzing porous fin thermal behavior.
  • Findings provide insights into optimizing fin design for enhanced thermal management.
  • The study supports the development of effective cooling solutions for electronic devices.