Efficient power analysis and five-objective optimization for a simple endoreversible closed Brayton cycle

Qiu, Xingfu; Chen, Lingen; Ge, Yanlin; Gong, Qirui; Feng, Huijun

doi:10.1016/j.csite.2022.102415

Cited by 21 publications

(7 citation statements)

References 53 publications

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“…

is the endoreversible cycle, while

is the actual irreversible one. Figure 1 b gives a system diagram of an irreversible simple CGT cycle [ 64 ]. The working fluid changes from states 2 to 3 through hot-side HEX, from states 3 to 4 through irreversible expansion of turbine, from states 4 to 1 through cold-side HEX, and finally from states 1 to 2 through irreversible compression of the compressor to complete the whole cycle.…”

Section: Cycle Modelmentioning

confidence: 99%

“…This reflects that the value of

has a very large impact on the value of

, has a greater impact on the values of

, and

, and has the least impact on the value of

. When

and

gradually raise to 1, the irreversible loss is smaller, and the result is closer to the endoreversible cycle [ 64 ], so the

and the

also raise, and the

is closer to 0.5. Comparing the effects of

and

on the

performance, it can be found that

has a greater impact on it.…”

Section: Efficient Power Performance Analysesmentioning

confidence: 99%

“…With the increase in OOs, there may be conflicts among different OOs. In order to coordinate the conflicts among OOs, some scholars used NSGA-II [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] to perform multi-objective optimization (MOO) for various HEG cycles. Ahmadi et al [ 58 ] studied the applicability of the Stirling-Otto combined cycle and performed MOO on

and

for combined cycle with six decision variables.…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al [ 62 ] performed MOO on

and

for a magnetohydrodynamic cycle. He et al [ 63 ] performed MOO on

and

for electronic HEG considering heat leakage loss, while Qiu et al [ 64 ] studied

performance of endo-reversible CGT, and performed MOO with five OOs of

and

.…”

Section: Introductionmentioning

confidence: 99%

“…As of now, there is no open literature concerning

analysis and MOO for simple irreversible CGT cycle. Taking the maximum

[ 41 , 42 , 53 , 64 ] as OO, although

of HEGs is sacrificed,

of the HEGs is greatly improved and the

reflects compromise between

and

of the HEGs. MOO [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ] can weigh the conflicts among different OOs and the MOO algorithm can be used to find the optimal solution when multiple OOs coexist, so as to optimize performance of the HEGs.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Power Characteristic Analysis and Multi-Objective Optimization for an Irreversible Simple Closed Gas Turbine Cycle

Qiu

Chen

et al. 2022

Entropy

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On the basis of the established irreversible simple closed gas turbine cycle model, this paper optimizes cycle performance further by applying the theory of finite-time thermodynamics. Dimensionless efficient power expression of the cycle is derived. Effects of internal irreversibility (turbine and compressor efficiencies) and heat reservoir temperature ratio on dimensionless efficient power are analyzed. When total heat conductance of two heat exchangers is constant, the double maximum dimensionless efficient power of a cycle can be obtained by optimizing heat-conductance distribution and cycle pressure-ratio. Through the NSGA-II algorithm, multi-objective optimizations are performed on the irreversible closed gas turbine cycle by taking five performance indicators, dimensionless power density, dimensionless ecological function, thermal efficiency, dimensionless efficient power and dimensionless power output, as objective functions, and taking pressure ratio and heat conductance distribution as optimization variables. The Pareto frontiers with the optimal solution set are obtained. The results reflect that heat reservoir temperature ratio and compressor efficiency have greatest influences on dimensionless efficient power, and the deviation indexes obtained by TOPSIS, LINMAP and Shannon Entropy decision-making methods are 0.2921, 0.2921, 0.2284, respectively, for five-objective optimization. The deviation index obtained by Shannon Entropy decision-making method is smaller than other decision-making methods and its result is more ideal.

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“…

is the endoreversible cycle, while

Section: Cycle Modelmentioning

confidence: 99%

“…This reflects that the value of

has a very large impact on the value of

, has a greater impact on the values of

, and

, and has the least impact on the value of

. When

and