Thermodynamic optimization of heat exchanger circuitry via genetic programming

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@Article{Giannetti:2024:applthermaleng,

• author = "Niccolo Giannetti and Adriano Milazzo and John Carlo Garcia and Cheol-Hwan Kim and Yuichi Sei and Koji Enoki and Kiyoshi Saito",
• title = "Thermodynamic optimization of heat exchanger circuitry via genetic programming",
• journal = "Applied Thermal Engineering",
• year = "2024",
• volume = "252",
• pages = "123623",
• keywords = "genetic algorithms, genetic programming, Thermodynamic analysis, Circuitry optimization, Zeotropic refrigerant mixture",
• ISSN = "1359-4311",
• URL = "https://www.sciencedirect.com/science/article/pii/S1359431124012912",
• DOI = "doi:10.1016/j.applthermaleng.2024.123623",
• abstract = "In this study, an evolutionary thermodynamic technique was developed for the optimisation of heat exchanger circuitry. The proposed technique is capable of handling the unrestrained implementation of genetic operators while ensuring circuitry feasibility and basic manufacturability. The optimisation tool was used to clarify the optimal heat transfer features in relation to the characteristics of the refrigerant and to provide a thermodynamic interpretation of the optimisation results to extract general design guidelines. Evaporator circuitry optimisation was conducted under given cooling capacity, superheating degree, and boundary conditions representative of air-conditioning applications. The consistency between the minimum entropy generation and the maximum coefficient of performance (COP) was demonstrated under these settings. Accordingly, heat exchanger configurations that take maximum advantage of the thermodynamic benefits of each refrigerant are proposed by optimising the distribution of friction and heat transfer irreversibility. Consequently, the evaporator outlet pressure increases, thus lowering the compression ratio and maximizing the COP. The developed optimisation method maximizes the benefits of low-GWP alternative refrigerants and shows that zeotropic mixtures may exhibit performance analogous to that of R32 and higher than that of R410A by approaching a Lorenz cycle operation",

}

Genetic Programming entries for Niccolo Giannetti Adriano Milazzo John Carlo Garcia Cheol-Hwan Kim Yuichi Sei Koji Enoki Kiyoshi Saito

Citations