An insight derived from <scp>CFD</scp> investigation on the regulation of vortex flow in jet impact negative pressure reactors: <scp>VG</scp> baffle structure
Bibliographic record
Abstract
Abstract The jet impact negative pressure reactor (JI‐NPR) is capable of achieving high efficiency and energy savings through continuous ammonia removal. A large number of multi‐scale vortex structures appear during the evolution of porous jet impingement under negative pressure conditions. The mixed model of mixture and the turbulence model of rsealizable k ‐ ε were used to simulate the flow field and vortex in the reactor. Firstly, the most suitable method to describe the multi‐scale vortex structure is determined. Next, the vortex core and other flow structures were modulated by configuring the spoiler elements. Specifically, the influence of parameters, including the quantity of spoiler elements (baffles), radial distances, and wing widths, on the turbulent flow field were investigated. Finally, the response surface method was used to construct the regression model equations for pressure drop and homogeneity. It is demonstrated that the Ω‐criterion offers a more accurate identification of the flow field inside the JI‐NPR. The baffle structure is conducive to reducing energy dissipation, destabilizing the flow field structure, and improving the interphase flow transfer efficiency. The relevant regression equations and optimal structural parameters are also determined. The present study can provide the foundation for the optimization of the geometry design of the JI‐NPR.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".