Active Injection Control for Enabling Clean Combustion in Ethanol-Diesel Dual-Fuel Mode
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In this work, an active injection control strategy is developed for enabling clean and efficient combustion on an ethanol-diesel dual-fuel engine. The essence of this active injection control is the minimization of the diffusion burning and resultant emissions associated with the diesel injection while maintaining controllability over the ignition and combustion processes. A stand-alone injection bench is employed to characterize the rate of injection for the diesel injection events, and a regression model is established to describe the injection timings and injector delays. A new combustion control parameter is proposed to characterize the extent of diffusion burning on a cycle-to-cycle basis by comparing the modelled rate of diesel injection with the rate of heat release in real time. The test results show that the proposed parameter, compared with the traditional ignition delay, better correlates to the enabling of low NOx and low smoke combustion. Particularly in the ethanol-diesel dual-fuel combustion, this new parameter can be controlled by actively adjusting the fuel ratio for each engine cycle. An active injection control strategy is therefore implemented to simultaneously control the engine load, combustion phasing, and the extent of diffusion burning via cycle-to-cycle adjustments on the fuelling rates, fuel ratio, and the diesel injection timing respectively. The control is validated by engine tests, and efficient combustion is achieved up to 19.5 bar IMEP with low NOx and low smoke emissions.</div></div>
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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 | 0.000 |
| 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 | 0.001 | 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".