High frequency reciprocating rig lubricity of diesel fuel with cetane improver additive
Bibliographic record
Abstract
Diesel fuel lubricates the fuel injection system in compression ignition engines. ASTM Test Method D6079 is a standardized bench test that uses the high-frequency reciprocating rig (HFRR) to evaluate the boundary lubricating properties of diesel fuel. The addition of 2-ethylhexyl nitrate (2-EHN) cetane improver additive (CIA) has been shown to increase the HFRR wear scar diameter (WSD) of some diesel fuels. However, this result was shown to be incongruent with pump rig tests conducted with the same diesel fuels. The objective of this research is to investigate the impact of CIAs on the HFRR WSD. HFRR lubricity tests were performed with No. 1-D or No. 2-D ultra-low sulfur diesel (ULSD) fuels with ester- or monoacid-type lubricity improver additives (LIAs) and nitrate-type CIA. Different strategies for modifying the diesel fuel or the test procedure to minimize the 2-EHN effect on HFRR WSD were evaluated. When 2-EHN CIA was added to the No. 2 diesel fuel with an appropriate treat rate of ester- or monoacid-type LIA, the HFRR WSD increased by up to 200 µm. However, the addition of 2-EHN to the No. 1-D ULSD fuel did not increase the HFRR WSD. The results show that doubling the LIA treat rate or a 2% blend of biodiesel are two workarounds that reduce the HFRR WSD back to acceptable levels. A slight modification to the Test Method that involves ramping up the reciprocating frequency over a period of time shows potential for minimizing the thermal decomposition of 2-EHN during a lubricity determination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".