Optimization performance and thermodynamic analysis of an irreversible nano scale Brayton cycle operating with Maxwell-Boltzmann gas

In last decades, nano technology developed. Since, nano scale thermal cycles will be possibly employed in the near future. In this research, a nano scale irreversible Brayton cycle is investigated thermodynamically for optimizing the performance of the aforementioned cycle. Ideal Maxwell-Boltzmann gas is employed as a working fluid in the system. In this paper, two scenarios are employed in the multi-objective optimization process; however, the outcomes of each of the scenarios are evaluated independently. In the first scenario, in order to maximize the dimensionless Maximum available work and energy efficiency of the system, multi-objective optimization algorithms is employed. Furthermore, in the second scenario, two objective functions comprising the dimensionless Maximum available work and the dimensionless Ecological function are maximized concurrently via employing multi objective optimization algorithms. The multi objective evolutionary approaches on the basis of non-dominated sorting genetic algorithm method are employed in this paper. Decision making is done via three methods including linear programming techniques for multidimensional analysis of preference and Technique for order of preference by similarity to ideal solution and Bellman-Zadeh. Finally, error analysis is implemented on the results obtained from each scenario.