Characteristics of SI engine fueled with BE50-Isooctane blends with different ignition timings
Bibliographic record
Abstract
The effect of various ignition timing on spark ignition (SI) engines with bioethanol-isooctane mixtures has been widely studied. In the present studies, we used three different ignition angle positions, namely 9°, 12°, and 15° BTDC to increase the combustion pressure in the combustion chamber. In addition to macroscopic observations through engine performance, observations are also carried out from a molecular perspective, i.e.; atomic, bond, and bond angle properties of bioethanol-isooctane fuel. The result shows that more atoms of the isooctane carbon chain are non-rotatable (23 atomic bonds) than the 8 bonds of the bioethanol carbon chain. Furthermore, isooctane also has a wider bond angle (around 121.1745°) than the bond angle of ethanol (around 110.0476°). The unique properties of the atoms in the carbon chains of these two fuels have a direct impact on engine performance. The results show that the viscosity of bioethanol is lower when compared to isooctane, which indicates that the bioethanol molecules are more reactive and flammable. The result also found that at an ignition angle of 12° the BE50 engine has the best performance. Moreover, the test results also show that bioethanol produces clean combustion as evidenced by the lowest CO and HC gas emissions.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".