<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The article presents the use of swarming algorithms in selecting the heat transfer coefficient, taking into account the boundary condition of the IV types. Numerical calculations were made using the proprietary TalyFEM program and classic form of swarming algorithms. A function was also used for the calculations, which, during the calculation, determined the error of the approximate solution and was minimalised using a pair of individually employed algorithms, namely artificial bee colony (ABC) and ant colony optimisation (ACO). The tests were carried out to select the heat transfer coefficient from one range. Describing the geometry for a mesh of 408 fine elements with 214 nodes, the research carried out presents two squares (one on top of the other) separated by a heat transfer layer with a κ coefficient. A type III boundary condition was established on the right and left of both edges. The upper and lower edges were isolated, and a type IV boundary condition with imperfect contact was established between the squares. Calculations were made for ABC and ACO, respectively, for populations equal to 20, 40 and 60 individuals and 2, 6 and 12 iterations. In addition, in each case, 0%, 1%, 2% and 5% noise of the reference values were also considered. The obtained results are satisfactory and very close to the reference values of the κ parameter. The obtained results demonstrate the possibility of using artificial intelligence (AI) algorithms to reconstruct the IV type boundary condition value during heat conduction modelling.</p>
</abstract>

The article presents the use of swarming algorithms in selecting the heat transfer coefficient, taking into account the boundary condition of the IV types. Numerical calculations were made using the proprietary TalyFEM program and classic form of swarming algorithms. A function was also used for the calculations, which, during the calculation, determined the error of the approximate solution and was minimalised using a pair of individually employed algorithms, namely artificial bee colony (ABC) and ant colony optimisation (ACO). The tests were carried out to select the heat transfer coefficient from one range. Describing the geometry for a mesh of 408 fine elements with 214 nodes, the research carried out presents two squares (one on top of the other) separated by a heat transfer layer with a κ coefficient. A type III boundary condition was established on the right and left of both edges. The upper and lower edges were isolated, and a type IV boundary condition with imperfect contact was established between the squares. Calculations were made for ABC and ACO, respectively, for populations equal to 20, 40 and 60 individuals and 2, 6 and 12 iterations. In addition, in each case, 0%, 1%, 2% and 5% noise of the reference values were also considered. The obtained results are satisfactory and very close to the reference values of the κ parameter. The obtained results demonstrate the possibility of using artificial intelligence (AI) algorithms to reconstruct the IV type boundary condition value during heat conduction modelling.

Selection of the Heat Transfer Coefficient Using Swarming Algorithms

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Selection of the Heat Transfer Coefficient Using Swarming Algorithms"}

The article presents the use of swarming algorithms in selecting the heat transfer coefficient, taking into account the boundary condition of the IV types....

Selection of the Heat Transfer Coefficient Using Swarming Algorithms

Pubblicato online: 17 ott 2022

Pagine: 325 - 339

Ricevuto: 24 giu 2022

Accettato: 10 ago 2022

DOI: https://doi.org/10.2478/ama-2022-0039

Parole chiave
swarm algorithm, ABC algorithm, ACO algorithm, heat transfer coefficient, computer simulation, numerical modelling

© 2022 Elżbieta Gawrońska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Selection of the Heat Transfer Coefficient Using Swarming Algorithms

Pubblicato online: 17 ott 2022

Pagine: 325 - 339

Ricevuto: 24 giu 2022

Accettato: 10 ago 2022

DOI: https://doi.org/10.2478/ama-2022-0039

Parole chiaveswarm algorithm, ABC algorithm, ACO algorithm, heat transfer coefficient, computer simulation, numerical modelling

© 2022 Elżbieta Gawrońska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
swarm algorithm, ABC algorithm, ACO algorithm, heat transfer coefficient, computer simulation, numerical modelling