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An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance
An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance
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An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance
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An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance
An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance

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An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance
An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance
Journal Article

An Optimization Study to Evaluate the Impact of the Supercritical CO2 Brayton Cycle’s Components on Its Overall Performance

2021
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Overview
The rising environmental problems due to fossil fuels’ consumption have pushed researchers and technologists to develop sustainable power systems. Due to properties such as abundance and nontoxicity of the working fluid, the supercritical carbon (sCO2) dioxide Brayton cycle is considered one of the most promising technologies among the various sustainable power systems. In the current study, a mathematical model has been developed and coded in Matlab for the recompression of the supercritical carbon dioxide Brayton cycle sCO2-BC. The real gas properties of supercritical carbon dioxide (sCO2) were incorporated into the program by pairing the NIST’s Refporp with Matlab© through a subroutine. The impacts of the various designs of the cycle’s individual components have been investigated on the performance of sCO2−BC. The impact of various sedative cycle parameters, i.e., compressor’s inlet temperature (T1), and pressure (P1), cycle pressure ratio (Pr), and split mass fraction (x), on the cycle’s performance (ηcyc) were studied and highlighted. Moreover, an optimization study using the genetic algorithm was carried out to find the abovementioned cycle’s optimized values that maximize the cycle’s per-formance under provided design constraints and boundaries.