CFD Modeling of Equivalence Ratio Effects on a Pressurized Turbulent Premixed Flame
Bibliographic record
Abstract
At a given power level, lean premixed (DLE) gas turbines vary equivalence ratio (ϕ) for optimal performance. This range is usually determined by variations in ambient conditions, acoustic response of the system, and emissions trade-off (e.g. between NOx and CO). In this work, the effects of ϕ variation on premixed jet flame lengths are investigated, by modeling the pressurized jet experiments of Griebel et al. [1]. While previous modeling of these experiments focused on a priori tabulated chemistry based methods, in this work we investigate an approach that represents finite-rate effects explicitly using skeletal chemistry (16 species, 41 reactions) in RANS and LES. Two equivalence ratios (ϕ = 0.56 and ϕ = 0.43) corresponding to the two extremes of flame lengths are chosen from the experimental database for 673K mixture preheat, 5 bar and 40 m/s jet velocity. A better correspondence with the experimentally measured flame length was achieved for ϕ = 0.43 than for ϕ = 0.56 indicating that the model is suitable when finite-rate effects are dominant but requires extensions for flames closer to the flamelet regime. It was found, further, that the RANS-EDC models failed to predict the confined turbulent jet development, as well as the flame lengths accurately, and demonstrated that scale resolution is required even for a relatively simple configuration.
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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 | 0.000 | 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 | 0.000 | 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".