Partially Stratified Charge Natural Gas Combustion: The Impact of Uncertainties on LES Modeling
Bibliographic record
Abstract
The aim of this work is to carry out statistical analyses on simulated results obtained from large eddy simulations (LES) to characterize spark-ignited combustion process in a partially premixed natural gas mixture in a constant volume combustion chamber (CVCC). Inhomogeneity in fuel concentration was introduced through a fuel jet comprising up to 0.6 per cent of the total fuel mass, in the vicinity of the spark ignition gap. The numerical data were validated against experimental measurements, in particular, in terms of jet penetration and spread, flame front propagation and overall pressure trace. Perturbations in key flow parameters, namely inlet velocity, initial velocity field, and turbulent kinetic energy, were also introduced to evaluate their influence on the combustion event. A total of 12 simulations were conducted. The results show how the perturbations affect flame front propagation, although the ensemble average shows that the position of the flame front can still be reasonably predicted with a single simulation. It has been also highlighted the role of turbulence in the evolution of the combustion process. This demonstrates the accuracy of LES in simulating such combustion process, a key step towards its application on more complex problems such as direct injection engines.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".