Estimation of Heat Transfer Coefficient in Inverse Heat Conduction Problem Using Quantum-Behaved Particle Swarm Optimization with Tikhonov Regularization
The quantum-behaved particle swarm optimization (QPSO) with Tikhonov regularization is used to solve the inverse heat conduction problem of estimating the time dependent heat transfer coefficient of a flat plate. The prior information about the functional form of the unknown is unavailable. The esti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2014-06-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1260/1748-3018.8.2.233 |
| Summary: | The quantum-behaved particle swarm optimization (QPSO) with Tikhonov regularization is used to solve the inverse heat conduction problem of estimating the time dependent heat transfer coefficient of a flat plate. The prior information about the functional form of the unknown is unavailable. The estimation is based on transient temperature measurements taken by the sensors imbedded in the plate, which are used in the least square model, minimized by QPSO. The detail of choosing the best regularization parameter by L-curve method is presented. Numerical experiments are performed to test the proposed method. Effects of the location and number of sensors are also investigated. Comparison with conjugate gradient method is given as well. |
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| ISSN: | 1748-3018 1748-3026 |