SAE 5W-30 Pumpability Studies in Modern 4- and 8-Cylinder Engines: Gelation Index and MRV Effects
Bibliographic record
Abstract
Four SAE 5W-30 formulations with a range of MRV and Gelation Index properties were tested in motored 4- and 8- cylinder engines at ambient temperatures between -35°C and -38°C (below anticipated minimum start temperatures (MSTs)). A slow-cooling profile was used to enhance gelation effects in the test engines, which were motored at normal fast idle speeds. Oil pressurization after the pump was relatively rapid in all cases and did not show a large dependence on oil type or temperature. However, pressurization times at the main gallery showed a correlation to interpolated D 4684 MRV viscosities of the test oils. No correlation was observed between pumpability characteristics and D 5133 gelation index. While the two 2.0L I-4 engines gave comparable pressurization characteristics, the two 4.6L V-8s were quite different from each other. Pumpability differences between the V-8 engines were due to the presence of a plate-type oil cooler in one engine, which reduced oil pressure by 200 KPa and lead to significantly longer pressurization times. At the lowest test temperatures, the 2.0L designs showed ‘pseudo air-binding’ behaviour with all the test oils, in which gallery pressure dropped near zero after an initial pressure spike; pressure before the filter, however, continued to be registered. Low temperature rheological analysis of some of the used test oils was conducted to understand changes occurring after the relatively brief engine operation. In some cases oils with higher gelation indices showed significant decreases after engine operation, while MRV values were relatively unaffected.
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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) | 0.000 | 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 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".