Theoretical study on leakage characteristics of an oil-injected single screw CO2 compressor
Bibliographic record
Abstract
Single Screw CO 2 Compressor (SSCC) is an essential component for utilizing CO 2 as working fluid. The miscibility of lubricating oil and CO 2 makes it more complicated to determine leaks in compressors. To investigate the leakage characteristics of the SSCC, a mathematical model for its operating process is developed. This model integrates an oil and gas two-phase leakage model, which considers the solubility of oil and CO 2 . The study examined SSCC leakage and performance across varying clearances, speeds, and suction/discharge pressures. Results show that, under constant operating conditions, only indirect leakage of CO 2 dissolved in the oil occurs at smaller clearances. However, as the clearance increases, direct leakage of CO 2 is observed. An increase in clearance results in greater leakage and a decrease in volume and adiabatic efficiency. In addition, the effect of the fitting clearance on leakage is significantly more pronounced than meshing clearance. At the same speed, an increase in the suction pressure or a decrease in the discharge pressure increases the compressor’s volumetric efficiency. As the suction or discharge pressure increases, the adiabatic efficiency first increases and subsequently decreases. The optimum suction and discharge pressures for achieving maximum adiabatic efficiency at varied rotational speeds differ.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".