Modélisation 0D/1D de la combustion diesel : du mode conventionnel au mode homogène
Bibliographic record
Abstract
The present thesis focuses on the 0D/1D Diesel combustion modeling of recent engines. The goal is to improve models accuracy while minimizing computation times in order to use simulation as a tool for engine pre-mapping. In the first part, a 0D model designed as a system simulation-oriented tool is proposed. The main contribution of this study is the modeling of the premixed part of the Diesel combustion. This model allows a detailed modeling of highly diluted combustion and combustion related to early injections. A new approach to quantify interactions between each spray in the case of multi injection strategies is also proposed. After calibration using a very small number of engine tests, results for the global combustion chamber model are compared with experimental measurements for the overall engine operating conditions. The second part of this work deals with the 1D Diesel combustion modeling. A Diesel spray model is at first developed and validated on experimental measurements. This model is then extended to reaction conditions using the coupling with a combustion model. The combustion model makes use of tabulated local reaction rates of fuel and is based on the Eddy Break-Up approach to describe the reaction rate related to the turbulent mixing process. The next step is the integration of the burning spray model into a Diesel engine combustion chamber model. A first validation using experimental results for a recent Diesel engine is done.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".