Ultra High Pressure Tribometer for Testing CO<sub>2</sub>Refrigerant at Chamber Pressures up to 2000 psi to Simulate Compressor Conditions
Bibliographic record
Abstract
The growing interest for using the natural refrigerant carbon dioxide (CO2) in refrigeration and air-conditioning applications instead of HFC refrigerants, due to environmental concerns, has led to the development of an ultra high pressure tribometer (UHPT) specifically tailored for testing in CO2 environment. The existing research on tribology related to CO2 environment has focused on investigations at relatively low chamber pressures due to equipment restrictions. The UHPT is a unique tribometer that has been custom designed and manufactured to allow testing under CO2 refrigerant at environmental pressures comparable to those found in compressors. A special housing, which surrounds the tribological surfaces subject to testing, is capable of withstanding chamber pressures up to 13.8 MPa (2000 psi) and can be temperature controlled from 0°C to 100°C via a thermal control system. A multi-axis strain gauge force transducer measures the applied load, frictional forces, and moments during friction testing, and computer control permits different loading profiles. Using this machine, experiments were performed at a range of pressures between 1.4 MPa (200 psi) and 6.9 MPa (1000 psi) of CO2 refrigerant. The results suggest a slightly better tribological performance at higher pressures compared to lower pressures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".