Simulation Data for Low-GWP Refrigerants Under Non-Ideal Conditions

Processed simulation results for six refrigerants, including R134a and R1234yf, generated using a non-ideal vapor compression model. The dataset contains thermodynamic performance, compressor power, environmental impact metrics, and sensitivity analyses. Author Xiang Ying published the data on figshare in 2026 to accompany a related manuscript.

Use Cases

• Ranking refrigerant alternatives based on integrated performance and environmental impact metrics like TEWI and RSPI.
• Conducting sensitivity analysis on system performance based on weighting factors and loss levels.
• Modeling thermodynamic behavior, including discharge temperature and volumetric cooling capacity, under non-ideal conditions.
• Comparing the performance of low-GWP refrigerants like R1234ze(E) and R290 against baseline R134a.

Strengths

• Includes results for six specific refrigerants: R134a, R1234yf, R1234ze(E), R152a, R600a, and R290.
• Simulation model considers multiple non-ideal factors: pressure drops, heat losses, and pressure-ratio-dependent compressor efficiency.
• Dataset size is 1.7 MB, indicating a focused and likely manageable collection of results.
• Released under a permissive CC-BY-4.0 license for broad reuse.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is simulation-based; its accuracy is contingent on the underlying model's assumptions and parameters.

Provenance

Source

figshare, author Xiang Ying.

Collection Method

Generated using a non-ideal vapor compression refrigeration simulation model.

Freshness

Last updated 2026-06-02 14:45:40; freshness should be verified.