A Numerical Study of the Effect of EGR on Flame Lift-off in n-Heptane Sprays Using a Novel PaSR Model Implemented in OpenFOAM
Bibliographic record
Abstract
The effect of exhaust gas recirculation (EGR) on flame lift-off in non-stationary n-heptane sprays was studied under Diesel engine-like conditions using numerical simulation involving complex chemistry and a novel partially stirred reactor (PaSR) model of subgrid turbulence-chemistry interaction. The flame-stabilization mechanism is a result of complex physical and chemical interactions and cannot be described by a simplified theory. To leading order it is determined by the chemical reaction time at the leading edge, the turbulent diffusivity, and the flow velocity; so that there exists a balance between the local convection velocity and the triple-flame propagation speed. In this study of ignition and flame formation and stabilization processes, the VSB2 stochastic blob-and-bubble spray model was used in combination with the volume reactor fraction model (VRFM) implemented in OpenFOAM. The reacting volume fraction in the VRFM was determined by solving for mixture fraction, progress variable, and their variances in order to estimate the non-uniformities of the fluid cell; rather than simply taking the ratio of the mixing and chemistry time-scales. The chemistry is described by a reduced n-heptane mechanism with 36 species involved in 81 reactions. The simulated lift-off trends are compared to available experimental data from the Engine Combustion Network, Sandia National Laboratories [1].
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".