(1-08) Numerical Study of n-Heptane Spray Auto-Ignition at Different Levels of Pre-Ignition Turbulence((DE-3)Diesel Engine Combustion 3-Modeling)
Bibliographic record
Abstract
It is reported in [1] that in n-heptane spray auto-ignition at constant volume under Diesel-like conditions is substantially affected by the pre-turbulence level. In these experiments, different levels of initial turbulent kinetic energy were generated using a multi hole plate that was moved through the combustion chamber. The turbulence generator was calibrated by LDA. The ignition delays were determined by studying the light emissions in the UV and visible wave length range by optical fibers and a photomultiplier. It was also possible to modify the pre-ignition level of turbulence by the injected spray especially in the regime of pilot injection. To evaluate these effects, a numerical study was done using the detailed chemistry approach incorporated into the KIVA-3 spray combustion code. The basic novelty of the proposed methodology, see [2, 3], is the application of a generalized partially stirred reactor, PaSR, model, to treat detailed oxidation kinetics of hydrocarbon fuels assuming that chemical processes proceed in two successive steps : the reaction act follows micro-mixing simulated on a sub-grid scale. If the all Re number RNG κ-ε model is employed, the micro-mixing time can be consistently defined giving the combustion model in a "well-closed" form. The detailed mechanism integrating the skeletal n-heptane oxidation chemistry with the kinetics of aromatics (up to four aromatic rings) formation for rich acetylene flames [4], consisting of 117 species and 602 reactions, was validated in numerical kinetic analysis, and the reduced mechanism (60 species, including soot forming agents up to the third aromatic ring and NOx species, 237 reactions) was used in the spray combustion simulations. The model application was illustrated by comparison of predicted and measured ignition delays at constant volume at different levels of preturbulence, and good agreement was found (see Fig. 1). Different definitions for the ignition delay were compared and the best agreement was achieved, when the start of ignition was defined as the moment when the average temperature in the combustion chamber exceeds the initial temperature by 1 %. The moderate increase in the level of pre-turbulence leads to a reduction in ignition delay owing to more rapid mixing in forming the ignitable mixture. This effect was also pronounced when the "pilot" injection with different injection schedules was analyzed.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".