Investigation of Diesel Fuel Lubricity and Evaluation of Bench Tests to Correlate with Medium and Heavy Duty Diesel Fuel Injection Equipment Component Wear - Part 1
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Bibliographic record
Abstract
<div class="htmlview paragraph">A study was conducted to investigate the effects of diesel fuel lubricity on diesel engine fuel injection equipment (FIE) wear and failure rates, for diesel fuels with poor to moderate lubricity characteristics, with and without lubricity additives. Five tests were used to evaluate diesel fuel lubricity characteristics: 1) a modified Falex Corporation Ball-on-Three-Disk (BOTD) lubricity test rig; 2) a high-speed Detroit Diesel Corporation (DDC) 8V71T engine test rig operated at maximum load and speed conditions under elevated fuel, coolant and ambient temperatures; 3) a Wärtsilä VASA 9R32, medium-speed, diesel engine electric power generation unit in Iqaluit, Nunavut, Canada, 4) a fuel pump rig (FPR) and 5) a high frequency reciprocating rig (HFRR).</div> <div class="htmlview paragraph">Conclusions drawn from the BOTD, DDC 8V71T, VASA 9R32, FPR and HFRR test results indicate that several lubricity additives, added to diesel fuel at concentrations from 70 ppm to 400 ppm, are capable of: 1) improving the lubricity of poor lubricity diesel fuels to satisfactory levels and 2) substantially reducing FIE wear rates. The BOTD was shown to have the best correlation with the DDC 8V71T and FPR tests with and without lubricity additives. The BOTD fuel lubricity bench test and associated proposed ASTM test method was found to be capable of determining fuel lubricity for fuels, with and without lubricity additives. The BOTD also appeared to be a reasonable indicator of the potential performance of the diesel fuel used in the VASA 9R32 tests. The BOTD was capable of doing this in a short time frame (less than 3 hours) using a 35 ml (1.2 US fl. oz) sample of test fuel.</div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it