Temperature‐dependent kinematic viscosity of selected biodiesel fuels and blends with diesel fuel
Bibliographic record
Abstract
Abstract The kinematic viscosities of four biodiesel fuels—two natural soybean oil methyl esters, one genetically modified soybean oil methyl ester, and one yellow grease methyl ester—and their 75, 50, and 25% blends with No. 2 diesel fuel were measured in the temperature range from 20 to 100°C in steps of 20°C. The measurements indicated that all these fuels had viscosity‐temperature relationships similar to No. 2 diesel fuel, which followed the Vogel equation as expected. A weighted semilog blending equation was developed in which the mass‐based kinematic viscosity of the individual components was used to compute the mixture viscosity. A weight factor of 1.08 was applied to biodiesel fuel to account for its effect on the mixture viscosity. The average absolute deviation achieved with this method was 2.1%, which was better than the uncorrected mass average blending equation that had an average absolute deviation of 4.5%. The relationship between the viscosity and the specific gravity of biodiesel fuels was studied. A method that could estimate the viscosity from the specific gravity of biodiesel fuel was developed. The average absolute deviation for all the samples using this method was 2.7%. The accuracy of this method was comparable to the weighted mass‐based semilog blending equation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".