Experimental and Numerical Investigation of Ethanol/Diethyl Ether Mixtures in a CI Engine
Bibliographic record
Abstract
The auto-ignition characteristics of diethyl ether (DEE)/ethanol mixtures are investigated in compression ignition (CI) engines both numerically and experimentally. While DEE has a higher derived cetane number (DCN) of 139, ethanol exhibits poor ignition characteristics with a DCN of 8. DEE was used as an ignition promoter for the operation of ethanol in a CI engine. Mixtures of DEE and ethanol (DE), i.e., DE75 (75% DEE + 25% ethanol), DE50 (50% DEE + 50% ethanol) and DE25 (25% DEE + 75% ethanol), were tested in a CI engine. While DE75 and DE50 auto-ignited at an inlet air pressure of 1.5 bar, DE25 failed to auto-ignite even at boosted pressure of 2 bar. The peak in-cylinder pressure for diesel and DE75 were comparable, while DE50 showed reduced peak in-cylinder pressure with delayed start of combustion (SOC). Numerical simulations were conducted to study the engine combustion characteristics of DE mixture. A comprehensive detailed chemical kinetic model was created to represent the combustion of DE mixtures. The detailed mechanism was then reduced using standard direct relation graph (DRG-X) method and coupled with 3D CFD code, CONVERGE, to simulate the experimental data. The simulation results showed that the effects of physical properties on DE50 combustion are negligible. Simulations of DE50 mixture revealed that the combustion is nearly homogenous, while diesel (n-heptane used as a surrogate) and DE75 showed similar combustion behavior with flame liftoff and diffusion controlled combustion. Diesel exhibited auto-ignition at an equivalence ratio of 2, while DE75 and DE50 showed auto-ignition in the equivalence ratio range of 1-1.5 and 0-1, respectively. The experiments and numerical simulations demonstrate how the high reactivity of DEE supports the auto-ignition of ethanol, while ethanol acts as a radical scavenger.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".