Flow Regimes and Heat Transfer Characteristics in Combustion Chambers
Bibliographic record
Abstract
This paper presents a numerical computations are performed to investigate the convective heat transfer characteristics of a gas turbine can combustor under non reacting flow conditions in a Reynolds number range 50,000 to 600,000 with a characteristic swirl number of 0.7. A sample of computational predictions of flow behaviors under reacting conditions are also shown for swirling furnace flow of 0.52. The RNG (K-ɛ Model) predictions are compared with the experimental data of local heat transfer distribution on the combustor liner wall. It was observed that the flow field in the combustor is characterized by an expanding swirling flow, which impinges on the liner wall close to the inlet of the combustor. The peak heat transfer augmentation ratio (compared with fully developed pipe flow) reduces from 10.5 to 2.7. Additionally, the peak location does not change with Reynolds number since the flow structure in the combustor is also a function of the swirl number. The size of the corner recirculation zone near the combustor liner remains the same for all Reynolds numbers and hence the location of shear layer impingement and peak augmentation does not change. The heat transfer coefficient distribution on the liner wall predicted from the RNG (K-ɛ Model) is in good agreement with experimental values. The location and the magnitude of the peak heat transfer are predicted in very close agreement with the experiments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".