Prediction and optimization of engine characteristics of a DI diesel engine fueled with cyclohexanol/diesel blends
Why this work is in the frame
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Bibliographic record
Abstract
This study uses cyclohexanol – a high-carbon, cyclic bio-alcohol which is a derivative of lignocellulosic biomass – in blended form with diesel to power a direct-injection single-cylinder diesel engine that is widely used in Indian agricultural sector. Experiments were conducted at the engine’s rated load using the blend composition of cyclohexanol in diesel (10%, 20% and 30% by vol.), EGR (10%, 15%, and 20%) and injection timing (19°, 21° and 23°CA bTDC) as controllable factors. The optimization criterion is to minimize smoke, NOx emissions, and BSFC. Response surface methodology coupled with desirability approach was used to predict and optimize NOx, smoke opacity and BSFC measured from the experiments. The top solutions predicted by desirability approach were validated by confirmatory experiments and were found to describe the experimental data to a reasonable accuracy of within 4%. With reference to diesel operation, it was found that 10% by vol. of cyclohexanol/diesel blend injected at 21°CA bTDC and 10% EGR reduced NOx (43.1%▼) and smoke opacity (32.4%▼) with an increase in BSFC (4%▲). Cyclohexanol/diesel blend at optimum conditions delivered better smoke reduction but with higher NOx and slight increase in BSFC Cyclohexanol/diesel blends can be recommended as a full-time fuel to substitute diesel subject to long-term durability tests in diesel engines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it