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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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Using artificial intelligence algorithms to reconstruct the heat transfer coefficient during heat conduction

Elzbieta Gawronska1, Maria Zych2, Robert Dyja1

  • 1Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Dabrowskiego 69, 42-201, Czestochowa, Poland.

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Summary
This summary is machine-generated.

Swarm intelligence algorithms, including artificial bee colony (ABC) and ant colony optimization (ACO), accurately reconstruct heat transfer coefficients in complex systems. These AI methods show potential for optimizing production and data analysis.

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Area of Science:

  • Computational Heat Transfer
  • Artificial Intelligence in Engineering
  • Optimization Algorithms

Background:

  • Accurate reconstruction of heat transfer coefficients is crucial for thermal system analysis.
  • Continuity boundary conditions with non-ideal contact present unique challenges in heat transfer modeling.
  • Swarm intelligence algorithms offer novel approaches to complex inverse problems in engineering.

Purpose of the Study:

  • To utilize swarm algorithms for reconstructing the heat transfer coefficient under continuity boundary conditions.
  • To evaluate the performance of Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO) algorithms in this reconstruction task.
  • To assess the impact of mesh size, population, iterations, and data disturbances on algorithm accuracy.

Main Methods:

  • Implementation of classical swarm algorithms (ABC and ACO) within custom application software.
  • Minimization of an error functional using ABC and ACO algorithms.
  • Numerical simulation of a cast-mold system with a heat-conducting layer, considering symmetry and specific boundary conditions (Robin, insulated, continuity).
  • Testing with varying finite element meshes, population sizes, iteration counts, and noise levels.

Main Results:

  • Both ABC and ACO algorithms successfully reconstructed the heat transfer coefficient with high accuracy.
  • Results demonstrated close agreement with reference values, even under data disturbances.
  • The study validated the effectiveness of swarm algorithms across different simulation parameters.

Conclusions:

  • Swarm intelligence algorithms, specifically ABC and ACO, are effective tools for reconstructing heat transfer coefficients.
  • These AI-driven methods show significant potential for optimizing production processes and enabling data-driven decisions in engineering.
  • The study highlights the robustness and accuracy of swarm algorithms in solving complex inverse heat transfer problems.