Optimization density power and thermal efficiency of an endoreversible Braysson cycle by using non-dominated sorting genetic algorithm

Abstract : Throughout the recent decade, numerous investigations were done on Braysson cycles that were yielded in various output power and thermal efficiency analyses. Throughout the current research, the dimensionless power density and thermal efficiency are optimized using \NSGA\ algorithm and finite time thermodynamic analysis. Compares optimum outcomes gained in this paper by executing LINMAP, \TOPSIS\ and Fuzzy Bellman–Zadeh decision makers with relevant outcomes gained in previous works. Furthermore, it was shown that the \FUZZY\ decision-maker provides better solutions in comparison with other implemented decision makers. Finally, analysis of deviation was carried out on the basis of the \MAPE\ technique and it was obtained that the average deviation of results generated by the aforementioned decision maker were 0.015% and 0.03% for the dimensionless power density and thermal efficiency, respectively. The highest deviations of outcomes gained by used decision makers are 0.12% and 0.04% for the thermal efficiency and dimensionless power density, correspondingly. Outcomes of this study may be useful for any further design of Braysson engines. Moreover, optimized results of this research are the basis of further research for comparison and validation.
Keywords : Braysson cycles
Type de document :
Article dans une revue
Energy Conversion and Management, Elsevier, 2015, 93, pp.31 - 39. 〈10.1016/j.enconman.2014.12.088〉
Liste complète des métadonnées

https://hal.univ-lorraine.fr/hal-01417571
Contributeur : Lemta Ul <>
Soumis le : jeudi 15 décembre 2016 - 17:06:36
Dernière modification le : jeudi 11 janvier 2018 - 06:27:33

Identifiants

Collections

Citation

Seyed Abbas Sadatsakkak, Mohammad H. Ahmadi, Roham Bayat, Seyed Mohsen Pourkiaei, Michel Feidt. Optimization density power and thermal efficiency of an endoreversible Braysson cycle by using non-dominated sorting genetic algorithm. Energy Conversion and Management, Elsevier, 2015, 93, pp.31 - 39. 〈10.1016/j.enconman.2014.12.088〉. 〈hal-01417571〉

Partager

Métriques

Consultations de la notice

44