# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 3 Small-Scale Vapor Compression Refrigeration (VCR) Systems
### Modeling
Moreover, semi-empirical sub-models for variable speed compressors and fixed-orifice expansion devices were incorporated into the cycle simulation model, which was then used to assess the effect of component characteristics (expansion orifice size, compressor stroke and speed) on system COP. The results indicate that a 5 \(\times\) 5 cm\({}^{2}\) heat source at 40 \({}^{\circ}\)C with the surroundings at 25 \({}^{\circ}\)C can be optimally cooled with a system with 110 W cooling capacity and a COP of 1.6. An innovative personal cooling system was designed comprising a miniature vapor compression system made of a rotary compressor, a microchannel heat exchanger as an evaporator, a thermal expansion valve, and a condenser made of helical refrigerant tubes immersed into a container filled with phase change material thermal storage [73]. Originally, the system worked with refrigerant R134a; however, the need to evaluate alternative low GWP refrigerants led to the development of a dynamic model using hierarchical structures consisting of component models connected with appropriate interfaces in Modelica [74]. Discretized models were used for the microchannel evaporator and PCM condenser dynamics. The model was validated with experimental data and used for evaluating system performance with R32, R1234 yf, R1234 ze(E), and R290. Even though R32 showed the highest COP with an increase of 8% vs. R134a, it was concluded that R1234 yf could be a drop-in replacement for R134a because it resulted in very similar system behavior. A good synergy between modeling and optimization techniques can assist in system redesign, leading to improved performance. For example, a recent study presented the theoretical modeling and optimization of a miniature VCR system [75]. The model was based on physical principles and empirical correlations while the optimization used heuristic multi-objective algorithms, such as the nondominated storing genetic algorithm II (NSGA-II), and multi-objective particle swarm optimization (OMOPSO). Before conducting the optimization, the authors compared their model with experimental results from other publications, resulting in a maximum error of 10%. The evaluation considered various scenarios, some seeking to maximize COP or cooling capacity, and others seeking to minimize refrigerant mass flow or compressor energy consumption. The results showed a cooling capacity of 500 W and a COP of around 7. [PERSON] et al. [76] conducted a multi-objective optimization of a portable vapor compression cooler focused on maximizing performance and minimizing weight. A steady state system simulation model was developed and validated against experimental data showing deviations within \(\pm 10\%\). The model was developed on EES software [77] and includes sub-models for the compressor and heat exchangers, while the refrigerant charge and capillary tube sub-models were replaced by prescribed values for evaporator superheating and condenser subcooling. The optimization was conducted to evaluate a change from the baseline reciprocating compressor to a mini-rotary compressor, which demanded a resize of the heat exchangers. The optimization was conducted to determine optimum heat exchanger areas and cabinet wall insulation thickness leading to minimum energy consumption for a fixed weight, or minimum weight for a fixed energy consumption. The resulting system configuration consumes 13% less energy than the baseline cooler if the weight remains unchanged, or a system with the same energy performance can have a 15% lower weight. Modeling has also been applied to the optimization of micro-scale vapor compression systems [78]. A numerical model was conducted on EES [79] using fundamental thermodynamic equations to define inlet and outlet states of each component, and calculate cooling power and cycle COP, neglecting pressure drop, heat losses, and superheating and subcooling values near the saturation curve.