Performance effects and economic viability of high-hydrated ethanol fumigation and diesel direct injection in a small compression ignition engine
Bibliographic record
Abstract
This study investigates the operation of a diesel engine (compression ignition) coupled to an alternator, using mixtures of diesel oil and hydrous ethanol as fuel, with high water content. The mixtures of ethanol and water were injected at the intake manifold (fumigation), while the diesel oil was injected directly in the combustion chamber. The mixtures of ethanol and water were prepared with 90, 80 and 70% of ethanol, at volumetric fraction. The analyzed parameters were energetic and exergetic efficiency, specific fuel consumption, exhaust gas opacity and exhaust gas temperature. The results have shown that the increase of water in hydrous ethanol causes reduction of efficiencies (energetic and exergetic) and increase the specific fuel consumption, however, the gas opacity and the exhaust gas temperature are reduced. Despite the reduction of efficiencies, the use of ethanol with high water fraction is viable because there is potential for fossil fuel substitution by a renewable source. Economically, it was verified that for each condition tested there is a maximum cost of ethanol for viability, and only in 4 out of 27 Brazilian states the use of fumigation of ethanol/water blends technique would be viable, but with the lowest water concentration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".