Simulations of Fuel/Air Mixing, Combustion, and Pollutant Formation in a Direct Injection Gasoline Engine
Bibliographic record
Abstract
Simulations of a Direct Injection Spark Ignition (DISI) engine have been performed for both early injection with homogeneous charge combustion and for late injection with stratified charge combustion. The purpose has been to study flow characteristics, fuel/air mixing, combustion, and NOx and soot formation. Focus is put on the combustion modeling. Two different full load cases with early injection are simulated, 2000 rpm and 6000 rpm. One load point with late injection is simulated, 2000 rpm and 2.8 bar net MEP. Three different injection timings are simulated at the low load point: 77, 82, and 87 CAD bTDC. The spray simulations are tuned to match measured spray penetrations and droplet size distributions at both atmospheric and elevated pressure. Boundary conditions for the engine simulations are taken from 1-D gas exchange simulations that are tuned to match engine tests. The engine simulations start 380 CAD's before TDC and include the closing of the exhaust valves, the whole intake stroke, injection, combustion, and expansion to 113 CAD aTDC. Combustion is simulated using a turbulent Flame Speed Closure (FSC) model. The model takes into account the effects of local mixture composition, turbulence, temperature, and pressure and predicts reasonably well the heat release rate in all the cases without tuning of any constants. From this it is concluded that it is well suited for SI engine combustion predictions. The model also handles the diffusion controlled post oxidation of the unburned fuel from overly rich regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".