CFD modeling of columns equipped with structured packings: I. Approach based on detailed packing geometry
Bibliographic record
Abstract
Abstract A three‐dimensional computational fluid dynamics (CFD) modeling approach based on detailed packing geometry for packed columns equipped with structured packings is presented. Simulations have been carried out by commercial CFD package FLUENT. Complex packing geometries were implemented by generating fine meshes for flow domains inside packed columns. Turbulence was modeled by the standard k‐ϵ model with FLUENT's enhanced wall treatment. Experiments on single‐phase pressure drop through Flexipac 3Y structured packing have been carried out in a 0.3‐m ID packed column with air flow to validate the modeling approach. A circular column and a rectangular column have been simulated with three different structured packings—BX packing with corrugation angles of 45° and 30° and Flexipac 3Y. One‐component single‐phase flow and two‐component single‐phase flow with species dispersion (mass transfer) have been simulated. Predicted pressure drop are in good agreement with the experimental data and data from literatures. The simulation results also demonstrated the ability of the CFD approach to capture the anisotropic characteristic of flow in structured packings. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".