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The optimization method based on the coupling of genetic algorithm and ant colony algorithm for the exhaust outlet space arrangement
- Source :
- AIP Advances, Vol 14, Iss 3, Pp 035050-035050-13 (2024)
- Publication Year :
- 2024
- Publisher :
- AIP Publishing LLC, 2024.
-
Abstract
- The exhaust outlet space arrangement is a crucial part to avoid casualties and economic losses in the event of contaminant gas leakage. To handle this problem, this work proposed a novel optimization method based on the coupling of the genetic algorithm (GA) and ant colony algorithm optimization (ACO), and the fitness function used in the optimization method is constructed as an implicit form. In this proposed optimization method, the ACO is used to obtain the implicit fitness function value, while the GA is selected to conduct the space arrangement optimization based on the iteration results transferred from ACO. With the help of this novel methodology, the influence of obstacles in space could be well considered into the space arrangement optimization, which leads to a reliable optimization result of the exhaust outlet configuration. Moreover, to validate the accuracy and efficiency of this coupling method, the optimization results are taken into the computational fluid dynamics numerical model to give a comparison with the conventional configuration. The comparison results indicate that the exhaust outlet arrangement following the optimization results shows a lower gas concentration value during the diffusion process. In addition, based on this optimal exhaust outlet space arrangement, the models with various leakage rates are also investigated and discussed in the numerical work. It is believed that the proposed method could provide an effective measure for the space arrangement optimization and the design of gas leakage protection.
Details
- Language :
- English
- ISSN :
- 21583226
- Volume :
- 14
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- AIP Advances
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.409b410ba2d144a29dad08af6d88cdf4
- Document Type :
- article
- Full Text :
- https://doi.org/10.1063/5.0196294