Effectiveness and efficiency of particle swarm optimization technique in inverse heat conduction analysis
| dc.contributor.author | Vakili, S. | |
| dc.contributor.author | Gadala, Mohamed S. | |
| dc.date.accessioned | 2021-12-26T18:11:17Z | |
| dc.date.accessioned | 2023-08-23T05:13:13Z | |
| dc.date.available | 2021-12-26T18:11:17Z | |
| dc.date.available | 2023-08-23T05:13:13Z | |
| dc.date.issued | 2009-08 | |
| dc.description | An inverse heat transfer problem is a problem in which the boundary conditions, initial conditions, geometry, or material properties are not fully specified. In this research, we focused mainly on finding the missing boundary conditions. These problems are called boundary inverse problems, and they have numerous applications in various fields of engineering. Because the effects of changes in boundary conditions are usually damped and lagged in the interior measurement points, the problem is typically an ill-posed one and displays a high level of sensitivity to measurement errors. In general, the uniqueness and the stability of an inverse heat conduction solution are not guaranteed [1]. This ill-posedness is commonly treated by using one or a combination of some regularization techniques, e.g., Tikhonov regularization [2], the future information method [1], or the iterative regularization technique [3]. In the last two decades, various numerical algorithms have been examined to obtain a reliable inverse heat transfer solution. The most commonly used methods are the least-square regularization method [1, 4], the sequential function specification approach [3–5], the space-marching technique [6], the conjugate gradient algorithm [7, 8], the steepest-descent method [9], the model-reduction method [10], genetic algorithms (GA) [11, 12], and artificial neural networks [13–15]. In this research, the particle swarm optimization (PSO) technique, a relatively new global optimizer, with very few applications in heat transfer literature, is applied. | en_US |
| dc.description.abstract | Three variations of the particle swarm optimization (PSO) method are used to solve the boundary inverse heat conduction problem, in one, two, and three dimensions. Both steady and transient problems are studied. It is shown that PSO can be successfully applied to inverse heat conduction problems, and can alleviate some of the stability problems of the classical approaches. The computational costs of the three variations of PSO (basic, repulsive, and complete repulsive) are compared with each other, and with an implementation of the genetic algorithm. For these problems, some variants of PSO are proven to be more efficient than other algorithms. Also, the effectiveness of PSO in dealing with noisy domains is investigated. | en_US |
| dc.identifier.citation | Vakili, S., & Gadala, M. S. (2009). Effectiveness and efficiency of particle swarm optimization technique in inverse heat conduction analysis. Numerical Heat Transfer, Part B: Fundamentals, 56(2), 119-141. | en_US |
| dc.identifier.doi | https://doi.org/10.1080/10407790903116469 | |
| dc.identifier.uri | https://dspace-uat.adu.ac.ae/handle/1/1992 | |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor and Francis Online | en_US |
| dc.subject | Effectiveness and Efficiency | en_US |
| dc.subject | Particle Swarm | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Inverse Heat | en_US |
| dc.subject | Conduction Analysis | en_US |
| dc.title | Effectiveness and efficiency of particle swarm optimization technique in inverse heat conduction analysis | en_US |
| dc.title.alternative | Numerical Heat Transfer, Part B: Fundamentals An International Journal of Computation and Methodology Volume 56, 2009 - Issue 2 | en_US |
| dc.type | Article | en_US |