TY - GEN
T1 - Parameter identification of the fractional order heat conduction model using a hybrid algorithm
AU - Brociek, Rafał
AU - Słota, Damian
AU - Capizzi, Giacomo
AU - Sciuto, Grazia Lo
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper authors present hybrid algorithm to solve heat conduction inverse problem. Considered heat conduction equation with Riemann-Liouville fractional derivative can be used to model heat conduction in porous materials. In order to effectively model the phenomenon of heat flow, all parameters of the model must be known. In considered inverse problem thermal conductivity coefficient, initial condition and heat transfer coefficient are unknown and must be identified having some information about output of the model (measurements of temperatures). In order to do that, function describing the error of approximate solution is constructed and then minimized. The hybrid algorithm, based on the probabilistic Ant Colony Optimization (ACO) algorithm and the deterministic Nelder-Mead method, is responsible for searching minimum of the objective function. Goal of this paper is reconstruction unknown parameters in heat conduction model with fractional derivative and show that hybrid algorithm is effective tool and works well in these type of problems.
AB - In this paper authors present hybrid algorithm to solve heat conduction inverse problem. Considered heat conduction equation with Riemann-Liouville fractional derivative can be used to model heat conduction in porous materials. In order to effectively model the phenomenon of heat flow, all parameters of the model must be known. In considered inverse problem thermal conductivity coefficient, initial condition and heat transfer coefficient are unknown and must be identified having some information about output of the model (measurements of temperatures). In order to do that, function describing the error of approximate solution is constructed and then minimized. The hybrid algorithm, based on the probabilistic Ant Colony Optimization (ACO) algorithm and the deterministic Nelder-Mead method, is responsible for searching minimum of the objective function. Goal of this paper is reconstruction unknown parameters in heat conduction model with fractional derivative and show that hybrid algorithm is effective tool and works well in these type of problems.
KW - Fractional heat conduction equation
KW - Identification Ant colony algorithm
KW - Inverse problem
UR - https://www.scopus.com/pages/publications/85076866232
U2 - 10.1007/978-3-030-30275-7_37
DO - 10.1007/978-3-030-30275-7_37
M3 - Conference contribution
AN - SCOPUS:85076866232
SN - 9783030302740
T3 - Communications in Computer and Information Science
SP - 475
EP - 484
BT - Information and Software Technologies- 25th International Conference, ICIST 2019, Proceedings
A2 - Damaševicius, Robertas
A2 - Vasiljeviene, Giedre
PB - Springer
T2 - 25th International Conference on Information and Software Technologies, ICIST 2019
Y2 - 10 October 2019 through 12 October 2019
ER -