Reynolds Stress Model in the Prediction of Confined Turbulent Swirling Flows
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Bibliographic record
Abstract
Strongly swirling vortex chamber flows are examined experimentally and numerically using the Reynolds stress model (RSM). The predictions are compared against the experimental data in terms of the pressure drop across the chamber, the axial and tangential velocity components, and the radial pressure profiles. The overall agreement between the measurements and the predictions is reasonable. The predictions provided by the numerical model show clearly the forced and free vortex modes of the tangential velocity profile. The reverse flow (or back flow) inside the core and near the outlet, known from experiments, is captured by the numerical simulations. The swirl number has been found to have a measurable impact on the flow features. The vortex core size is shown to contract with the swirl number which leads to higher pressure drop, higher peak tangential velocity, and deeper radial pressure profiles near the axis of rotation. The adequate agreement between the experimental data and the simulations using RSM turbulence model provides a valid tool to study further these industrially important swirling flows.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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