Optimization of shell ignition model and its application to the prediction of ignition performance of renewable fuels in an HCCI engine
Bibliographic record
Abstract
Ignition timing control is crucial for compression ignition (CI) engines. Modelling ignition phenomenon is essential for design and operation of CI engines. Accurate prediction of ignition phenomenon requires a robust ignition model. Therefore, detailed and semi-detailed chemical kinetics have been developed for various fuels, such as [1-3]. These kinds of kinetics models are able to reasonably predict ignition performance in engines. However, detailed or semi-detailed kinetics models usually include many species and reactions. The use of these kinds of detailed or semi-detailed models in the prediction of ignition and combustion process in engines is still too computationally expensive today. Therefore, reduced or simplified kinetics models that include limited number of species and are capable of reasonably describing ignition and combustion performance in engines are still important. Given the fact that simplified one- or two-step models usually cannot reasonably describe the main features of ignition process, especially for those fuels with two stage combustion processes, such as diesel fuels, reduced kinetic model is a more appropriate choice for the prediction of ignition process in practical applications. The so-called Shell ignition model developed in 1970s [4,5] is a reduced thermo-kinetic model that includes limited species and is capable of reasonably predicting ignition process in engines, provided that appropriate rate expressions are provided. This study focuses on the practical application of the Shell model to predict the ignition of various renewable fuels in homogeneous charge compression ignition (HCCI) engines. The rate expressions of the Shell model will be obtained by an optimization scheme based on the ignition delay data obtained from Ignition Quality Tester (IQT).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".