2023
DOI: 10.1016/j.enconman.2023.116857
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Unsteady and three-dimensional computational fluid dynamics modelling of scroll expander for low-grade waste heat recovery transcritical carbon dioxide micro-scale power system

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Cited by 7 publications
(3 citation statements)
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“…They are geometry design, CFD validations, data science, and machine learning. Detailed descriptions of the geometry design, numerical methods, and CFD validations can be found in Du et al [25]. The following subsections briefly introduce the data science and machinery learning process, while the geometry and CFD settings are briefly introduced.…”
Section: Methodsmentioning
confidence: 99%
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“…They are geometry design, CFD validations, data science, and machine learning. Detailed descriptions of the geometry design, numerical methods, and CFD validations can be found in Du et al [25]. The following subsections briefly introduce the data science and machinery learning process, while the geometry and CFD settings are briefly introduced.…”
Section: Methodsmentioning
confidence: 99%
“…Two objectives are defined as f η I (isentropic efficiency) and f cost (manufacturing cost). The reason to choose isentropic efficiency as an objective is that it is the key parameter to reflect the machinery's performance and its trend is similar to the second-law efficiency from the thermodynamic analysis [25]. To simplify the equations, f 1 and f 2 replace f η I and f cost .…”
Section: Surrogate-assisted Multi-objective Optimisationmentioning
confidence: 99%
“…Song et al (10) performed three-dimensional numerical simulations of a Vortex Swellers with different suction port positions based on CFD. Du et al (11) compared the performance differences between T-CO2 and R123. CO2 and R123 performance differences, T-CO2 exhibited better performance with an average isentropic efficiency 14% higher than that of R123.…”
Section: Introductionmentioning
confidence: 99%