Performance Analysis of Natural-Refrigerants-Based Vortex Tube Expansion Refrigeration Cycles
Bibliographic record
Abstract
The energetic analyses and comparison of three natural refrigerants, ammonia, propane and isobutane based vapour compression refrigeration cycles are presented in this article using a vortex tube as an expansion device. A simple thermodynamic model has been used for analyses of two vortex tube expansion refrigeration cycle layouts based on the Maurer model (1999) and the Keller model (1997). Effects of various operating and design parameters of the COP improvement using vortex tube instead of expansion valve are presented. Results show that the COP improvement over basic expansion cycle increases with increase in cycle temperature lift for both cycle layouts. The COP improvement of CYC1 can be realized for certain operating temperature combinations. Effects of design parameters on the performance improvement are negligible. Study shows that the COP improvement using vortex tube as an expansion device are dependent on the refrigerant varieties, operating conditions as well as cycle configurations. Using the vortex tube as an expansion device, isobutane yields a maximum COP improvement of 12.2% for CYC2 followed by propane (11.5% for CYC2), whereas ammonia yields negligible improvement for studies ranges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".