Measurement of the Rate of Multiple Fuel Injection with Diesel Fuel and DME
Bibliographic record
Abstract
The accuracy of the injection rate meter based on W. Zeuch's method in the measurement of multiple injection rate and amount was calibrated using a small cam driven piston that is driven by an electric motor. For the pre- or early-injection, a sensor with a high sensitivity can be applied to measure the small pressure increase due to the small injection amount. In case of the multiple injection that has the post and/or late injection, a pressure sensor with a low sensitivity must cover not only the large pressure increase due to the main injection but also the small pressure increase due to the post and/or late injection because the output of the high sensitivity sensor is saturated after the main injection. So the linearity of the low sensitivity pressure sensor was calibrated with the cam driven piston prior to the experiment with the actual injection system. Then the measurement of the rate of multiple fuel injection was made successfully up to 3 stages per cycle and the reliability of the system developed was demonstrated. The injection rate meter is also applied to the measurement of the injection rate of DME that is regarded as one of the future alternative fuels for Diesel engines. As its material characteristics such as boiling temperature or bulk modulus of elasticity of volume are different from the conventional Diesel fuel, the dimensions of measuring system may have to be modified appropriately to achieve the reliable results. Some of the results of the preliminary test to measure multiple injection with a common rail injection system using DME are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".