RANS and LES Modeling of a Linear-Array Swirl Burner Using a Flamelet-Progress Variable Approach
Bibliographic record
Abstract
Multiple, interacting flames in DLE systems can increase flame surface area and promote mixing of hot-products into the reactants — leading to an efficient usage of combustion volume and improved injector performance. An optically-accessible, confined, linear array of five swirl nozzles was recently built [1] to investigate flame dynamics and validate computational strategies. The present work focuses on modeling a dataset representative of lean gas turbine conditions, using a flamelet approach. A preheated (500K), premixed fuel-air mixture (ϕ = 0.55, Tflame = 1732K) at atmospheric pressure was injected through the swirlers at 40 m/s into a rectangular chamber. High-speed laser measurements of the flow (3 component velocity field from 10 kHz stereoscopic particle image velocimetry (S-PIV)) and flame (planar laser induced fluorescence of the hydroxyl radical (OH-PLIF)) were used for model validation. The objectives of this work: (1) Evaluate a flamelet-progress variable method based on flamelet-generated manifolds (FGM) and examine its sensitivity to models for micro (scalar dissipation) and large scale mixing (anisotropic RANS vs LES) and (2) Obtain insight into the velocity field and flame stabilization in an interacting system. Computations indicate that high-swirl nozzles produce bluff-body flames anchored to shear-layer vortices due to an arrested flow expansion. The anisotropic RANS turbulence model under-predicts the recirculation zone strength but predicts flow development and Reynolds stress profiles fairly well. While LES is more accurate overall, both models over-predict flow fluctuations in the transitional flow at the end of the recirculation bubble where flow becomes axially positive. The flamelet approach predicts the flame-shape and length correctly but over-predicts the reaction rate in-between swirlers. The effect of including a reactive SDR model is to significantly increase flame-flow interaction (higher scalar variance) but does not appear to influence the overall shape or location of the flame.
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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".