CFD Predictions of CO Emission Trends in an Industrial Gas Turbine Combustor
Bibliographic record
Abstract
CFD predictions of emissions such as NOx and CO in industrial lean-premixed gas turbine combustors depend heavily on the degree to which the complexity of turbulent mixing and turbulence-chemistry interaction in the flow-field is modeled. While there is much difficulty in obtaining detailed and accurate internal data from high pressure combustors, there is a definite need for accurately understanding the flow physics towards the improvement of design. This work summarizes some experience with using the RANS and LES approaches in a commercial code, Fluent 6.3, to predict CO emissions and temperature trends in the two-stage Rolls-Royce RB211-DLE combustor. The predictions are validated against exit emissions (obtained from exhaust gas analysis) and some thermal paint tests for qualitative agreement on flame-stabilization. The upstream geometry (plenum and counter-swirlers) was included in order to minimize the effect of boundary conditions on the combustion zone. The presumed pdf approach as well as finite-rate chemistry models using the eddy dissipation concept were used to compare the predictions. It was found that there was a very significant benefit in moving to more advanced turbulence modeling methods to obtain realistic predictions in a confined, swirling burner. Thermal paint tests indicated that flame stabilization and temperatures (and therefore CO) was incorrectly predicted in the RANS context. LES results, on the other hand, more accurately predicted flame stabilization with corresponding improvements in the exit CO predictions. Ongoing work focuses on the variations that can be expected by varying discretization schemes, combustion models and sub-grid turbulence models as well as obtaining detailed internal data suitable for LES comparisons.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".