Mechanics & Industry
Volume 18, Number 2, 2017
|Number of page(s)||16|
|Published online||31 January 2017|
Thermodynamic analysis and optimization of an irreversible nano scale dual cycle operating with Maxwell-Boltzmann gas
1 Department of Renewable Energies, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
2 Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
3 Laboratoire d’Energétique et de Mécanique Théorique et Appliquée, ENSEM, 2 avenue de la Forêt de Haye, 54518 Vandoeuvre-lès-Nancy, France
4 Department of Mechanical and Manufacturing Engineering, Engineering Faculty, Bilecik S.E. University, Bilecik, Turkey
a Corresponding author: firstname.lastname@example.org
Received: 14 October 2015
Accepted: 14 March 2016
In last decades, nano technology was developed. Since, nano scale thermal cycles will be possibly employed in near future. In this research, a nano scale irreversible dual cycle is investigated thermodynamically for optimization of performance. Ideal Maxwell-Boltzmann gas is used for working fluid in the system. It is chosen as working fluid. In this paper, two scenarios are introduced for optimization process. The outcomes of each of the scenarios are evaluated independently. Throughout first scenario, in order to maximize the dimensionless output work and first law efficiency of the system, multi-objective optimization algorithms are employed. Furthermore, in second scenario, two objective functions comprising the dimensionless output work are the dimensionless ecological function are maximized concurrently via employing multi objective optimization algorithms. The multi objective evolutionary approaches (MOEAs) on the basis of NSGA-II method are employed in this paper Decision making is done via three methods including LINAMP and TOPSIS and FUZZY. Finally, error analysis is implemented on the results obtained in this research
Key words: Dual cycle / dimensionless ecological function / dimensionless output work / optimization / decision making
© AFM, EDP Sciences 2017
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