Parametric study of the impact of EGR and fuel properties on diesel engine performance using a predictive thermodynamic model
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a detailed quasi-dimensional model that can assist in the design process as it allows predicting the performance and emissions of compression ignition (CI) engines functioning with different fuels, such as kerosene and Diesel. The proposed model includes a new dedicated fuel injection mass flow rate sub-model that is coupled to the multi-zone spray packet concept for spray development. Moreover, fuel spray development and its interaction with in-cylinder swirl is considered and allows studying the influence of combustion chamber design, fuel injection strategy, as well as fuel properties. The model is validated against single and double injection strategies using kerosene and diesel fuels and is shown to be capable of predicting engine performance with a good accuracy. The results obtained from the parametric studies have shown proper trend with respect to the effect of the bowl-to-bore diameter ratio, EGR rate and temperature or fuel properties. This latter predicts that fuels with higher lower heating values (LHVs) can decrease NO and soot emissions by using retarded injection timing, while the boiling temperature has a small effect on the evaporation and mixture formation process. Finally, a fuel with a high enthalpy of vaporization can achieve lower soot emissions by increasing the swirl ratio or increasing the injection timing although doing so is detrimental to the power output.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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