Exergoeconomic analysis and genetic algorithm power optimization of an irreversible regenerative Brayton cycle

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In this study, the performance of an irreversible regenerative Brayton cycle is sought through power maximizations using finite-time thermodynamic concept in finite-size components. Optimizations are performed using a genetic algorithm. In order to take into account the finite-time and finite-size concepts in the current problem, a dimensionless mass-flow rate parameter is used to deploy time variations. The results of maximum power state optimizations are investigated considering the impact of dimensionless mass-flow rate parameter variations. One can see that the system performance shows high values of the dimensionless mass-flow rate parameter because of low power production while the high total cost rate is not reasonable. The other objective (besides power maximization) of the current study is to prepare finite-time thermodynamics for studying more practical systems using new thermodynamic modelling, exergy, and cost analyses of the current system.