Volume 17, Issue 11 (1-2018)
Modares Mechanical Engineering 2018, 17(11): 408-418 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Mazidi Sharfabadi M, Alizadeh M, Nourpour L. The Modification of the Conventional Genetic Algorithm for Solving Inverse Heat Transfer Problems. Modares Mechanical Engineering 2018; 17 (11) :408-418
URL: http://mme.modares.ac.ir/article-15-9521-en.html
URL: http://mme.modares.ac.ir/article-15-9521-en.html
1- Assistant Professor / Research Institute of Petroleum Industry (RIPI)
2- Energy conversion group, department of mechanical engineering, Iran university of science and technology
3- School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
2- Energy conversion group, department of mechanical engineering, Iran university of science and technology
3- School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: (5798 Views)
In this study, the inverse heat transfer problem of the estimation of unknown heat flux imposed on the boundary of a one-dimensional slab is solved by the genetic algorithm and two modified versions of this algorithm and the results obtained from different versions of the genetic algorithm are compared with each other. These two modified versions are developed based upon genes rearrangement approach. In this approach, an additional cost function is added to the conventional genetic algorithm to increase its computational efficiency. The results obtained by using errorless simulated temperature measurements show that modified genetic algorithms can improve the convergence and accuracy of the inverse solution in comparison with the conventional genetic algorithm and they give accurate estimations for the supposed heat flux even by using a small number of generations and moderate population size. The results show that modified genetic algorithm (2) provides better response to all the parameters of the solution evaluation in comparison with the conventional genetic algorithm and other modified version. In addition, in this study, the effect of adding Tikhonov regularization term to the objective function on the stability of the solution is investigated. Although only a simple one-dimensional problem has been solved in this study to demonstrate the approach of genes rearrangement, but this approach is expected to succeed in the inverse solution of complicated multidimensional problems.
Received: 2017/08/14 | Accepted: 2017/10/18 | Published: 2017/11/27
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |