<scp>CFD</scp> investigation of flow hydrodynamics and optimization in an industrial‐scale annular lance with swirl flow
Bibliographic record
Abstract
Abstract Swirling flow has been applied in various fields due to its ability to enhance mass, heat, and momentum transfer performance. However, the generation of swirling flow occurs at the price of augmenting the pressure drop, and enhancing the friction and shear intensity of the jet with respect to the reactor wall. In the present work, the impact of geometrical configurations of the swirler on the hydrodynamics of the fluid in an industrial‐scale annular lance is investigated via the computational fluid dynamics method, with the discussion of the friction coefficient of the lance walls. It shows that the axial flow injected from the central lance is transformed into a weak swirl flow upon the introduction of swirl flow generated in the casing pipe. Within the mixing region, the interaction between axial and swirl flows results in elevated turbulent kinetic energy. Notably, under varying geometrical configuration conditions, the pressure drop between the inlet of the central pipe and the outlet is maximized. Additionally, the highest friction factor appears at a height of 1.35 m along the middle shell, with a value of 96.67.
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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 | 0.000 |
| 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 | 0.001 |
| 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".