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- Title
Two ımproved teaching–learning-based optimization algorithms for the solution of ınverse boundary design problems.
- Authors
Amiri, Hossein; Radfar, Navid; Arab Solghar, Alireza; Mashayekhi, Mostafa
- Abstract
In this study, to enhance the searching ability, convergence velocity, computational efficiency, and global performance of the standard Teaching–Learning-Based Optimization (TLBO) algorithm, two modified TLBO algorithms designated as, TLBO-M1 and TLBO-M2, are developed. The standard and the two proposed TLBO algorithms are applied to solve inverse radiative boundary design problems in two-dimensional radiant furnaces with grey absorbing and emitting media. The goal of the inverse design problem is to find the best distribution of heat flux over the heater surface of the radiant enclosure which produces the desired temperature and heat flux distributions over the design surface. Radiation is considered the prevailing mode of heat transfer and thus the media inside the enclosure is at radiative equilibrium. Radiative heat transfer within the enclosure is modelled using the radiative transfer equation and is solved using the discrete ordinates method (DOM). To evaluate the performance of the proposed algorithms, a validation problem and two inverse design problems with regular and irregular enclosures are considered. The results demonstrate that the TLBO-M1 algorithm is more effective and robust compared to the standard TLBO and TLBO-M2.
- Subjects
OPTIMIZATION algorithms; HEAT radiation &; absorption; RADIATIVE transfer equation; INVERSE problems; HEAT flux; HEAT transfer
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2023, Vol 27, Issue 17, p12133
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-023-08415-2