Abstract
A vapor compression cycle, which is typically utilized for the heat pump, air conditioning and refrigeration systems, has inherent thermodynamic losses associated with expansion and compression processes. To minimize these losses and improve the energy efficiency of the vapor compression cycle, an ejector can be applied. However, due to the occurrence of complex physics i.e., non-equilibrium flashing compressible flow in the nozzle with possible shock interactions, it has not been feasible to model or optimize the design of a two-phase ejector. In this study, a homogeneous, non-equilibrium, two-phase flow computational fluid dynamics (CFD) model in a commercial code is used with an in-house empirical correlation for the mass transfer coefficient and real gas properties to perform a geometric optimization of a two-phase ejector. The model is first validated with experimental data of an ejector with R600a as the working fluid. After that, the design parameters of the ejector are optimized using multi-objective genetic algorithm (MOGA) based online approximation-assisted optimization (OAAO) approaches to find the maximum performance.
| Original language | English |
|---|---|
| Pages (from-to) | 272-282 |
| Number of pages | 11 |
| Journal | Applied Thermal Engineering |
| Volume | 109 |
| DOIs | |
| Publication status | Published - 2016 Oct 25 |
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Keywords
- Ejector
- MOGA
- OAAO
- R600a
- Two-phase CFD
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Industrial and Manufacturing Engineering
Cite this
Optimization of two-phase R600a ejector geometries using a non-equilibrium CFD model. / Lee, Moon Soo; Lee, Hoseong; Hwang, Yunho; Radermacher, Reinhard; Jeong, Hee Moon.
In: Applied Thermal Engineering, Vol. 109, 25.10.2016, p. 272-282.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimization of two-phase R600a ejector geometries using a non-equilibrium CFD model
AU - Lee, Moon Soo
AU - Lee, Hoseong
AU - Hwang, Yunho
AU - Radermacher, Reinhard
AU - Jeong, Hee Moon
PY - 2016/10/25
Y1 - 2016/10/25
N2 - A vapor compression cycle, which is typically utilized for the heat pump, air conditioning and refrigeration systems, has inherent thermodynamic losses associated with expansion and compression processes. To minimize these losses and improve the energy efficiency of the vapor compression cycle, an ejector can be applied. However, due to the occurrence of complex physics i.e., non-equilibrium flashing compressible flow in the nozzle with possible shock interactions, it has not been feasible to model or optimize the design of a two-phase ejector. In this study, a homogeneous, non-equilibrium, two-phase flow computational fluid dynamics (CFD) model in a commercial code is used with an in-house empirical correlation for the mass transfer coefficient and real gas properties to perform a geometric optimization of a two-phase ejector. The model is first validated with experimental data of an ejector with R600a as the working fluid. After that, the design parameters of the ejector are optimized using multi-objective genetic algorithm (MOGA) based online approximation-assisted optimization (OAAO) approaches to find the maximum performance.
AB - A vapor compression cycle, which is typically utilized for the heat pump, air conditioning and refrigeration systems, has inherent thermodynamic losses associated with expansion and compression processes. To minimize these losses and improve the energy efficiency of the vapor compression cycle, an ejector can be applied. However, due to the occurrence of complex physics i.e., non-equilibrium flashing compressible flow in the nozzle with possible shock interactions, it has not been feasible to model or optimize the design of a two-phase ejector. In this study, a homogeneous, non-equilibrium, two-phase flow computational fluid dynamics (CFD) model in a commercial code is used with an in-house empirical correlation for the mass transfer coefficient and real gas properties to perform a geometric optimization of a two-phase ejector. The model is first validated with experimental data of an ejector with R600a as the working fluid. After that, the design parameters of the ejector are optimized using multi-objective genetic algorithm (MOGA) based online approximation-assisted optimization (OAAO) approaches to find the maximum performance.
KW - Ejector
KW - MOGA
KW - OAAO
KW - R600a
KW - Two-phase CFD
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UR - http://www.scopus.com/inward/citedby.url?scp=84990055876&partnerID=8YFLogxK
U2 - 10.1016/j.applthermaleng.2016.08.078
DO - 10.1016/j.applthermaleng.2016.08.078
M3 - Article
AN - SCOPUS:84990055876
VL - 109
SP - 272
EP - 282
JO - Applied Thermal Engineering
JF - Applied Thermal Engineering
SN - 1359-4311
ER -