Experimental study on fuel line pressure fluctuation of biodiesel–DME blends in a common-rail injection system
Bibliographic record
Abstract
The fuel line pressure fluctuations at the injector outlet of biodiesel–dimethyl ether (DME) blends in a common-rail injection system are investigated using the wavelet packet transform. The effects of injection condition and DME blending ratio on the pressure fluctuation characteristics are determined. The results show that the pressure fluctuation intensity determines wavelet packet energy distribution in the three subsignals. The overall pressure fluctuation is reflected by subsignal Sig1, which is composed of subsignals Sig2 and Sig3. Sig2 exhibits medium-frequency pressure fluctuations, and mainly occurs in the pressure rise stage and the pressure drop stage. Sig3 exhibits high-frequency pressure fluctuations, and mainly locates in the pressure stabilization stage. The pressure fluctuations at high injection pressure are more significant compared with those at low injection pressure. Biodiesel–DME blends display similar effects of injection pressure. With the increase of injection pressure, the mean energy and the standard deviation of Sig1, Sig2, and Sig3 increase, especially those of Sig1 and Sig3. When DME is blended in the biodiesel, the pressure fluctuations for biodiesel–DME blends are more significant compared with biodiesel. With the increase of DME blended ratio, the mean energy and the standard deviation of Sig1 and Sig3 gradually increase.
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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 | 0.001 |
| 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".