Thermal conductivities estimation in orthotropic materials making use of global optimization algorithms

There are situations where the knowledge of thermodynamic properties such as thermal conductivity for the present case is required. In some of them an additional requirement appears, as the measurement has to be made along the three perpendicular space axes. In the present article, three thermal con...

Full description

Autores:
Vega-Suarez, Jefferson
García-Morantes, Edgar
Correa-Cely, Rodrigo
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/68506
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/68506
http://bdigital.unal.edu.co/69539/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
Problema Inverso
Optimización
Levenberg-Marquardt
Algoritmo Cuckoo
Conductividad Térmica
inverse problem
optimization
Levenberg-Marquardt
cuckoo algorithm
thermal conductivity
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:There are situations where the knowledge of thermodynamic properties such as thermal conductivity for the present case is required. In some of them an additional requirement appears, as the measurement has to be made along the three perpendicular space axes. In the present article, three thermal conductivities that appear in orthotropic materials were predicted by solving an inverse heat transfer problem. This inverse problem was solved using the Cuckoo algorithm, the deterministic Levenberg-Marquardt, and with the new hybrid of these two. It was found that these three strategies produce excellent results when compared to each other. Nevertheless, the hybrid algorithm proved to be more efficient than its precursors in solving the present problem. The hybrid algorithm consumed in average less computing time compared to the metaheuristic algorithm and extended the search range in comparison to the deterministic one, always maintaining precision in its results.