Effect of Tube-to-Pellet Diameter Ratio on Turbulent Hydrodynamics in Packed Beds: A Magnetic Resonance Velocity Imaging Study
Bibliographic record
Abstract
Abstract The hydrodynamics in packed reactors strongly influences reactor performance. However, limited experimental techniques are capable of non-invasively measuring the velocity field in optically opaque packed beds at the turbulent flow conditions of commercial relevance. Here, compressed sensing magnetic resonance velocity imaging has been applied to investigate the hydrodynamics of turbulent flow through narrow packed beds of hollow cylindrical catalyst support pellets as a function of the tube-to-pellet diameter ratio, $$N$$ N , for $$N=$$ N = 2.3, 3.7, and 4.8. 3D images of time-averaged velocity for the gas flow through the beds were acquired at constant Reynolds number, $$R{e}_{\mathrm{p}}=$$ R e p = 2500, at a spatial resolution of 0.70 mm ( $$\tt x$$ x ) $$\times$$ × 0.70 mm ( $$\tt y$$ y ) $$\times$$ × 1.0 mm ( $$\tt z$$ z ). The resulting flow images give insight into the bed and pellet scale hydrodynamics, which were systematically compared as a function of $$N$$ N . Some changes in hydrodynamics with $$N$$ N were observed. Namely, the near-wall hydrodynamics changed with $$N$$ N , with the $$N=$$ N = 4.8 bed showing higher velocity at the wall compared to the $$N=$$ N = 2.3 and $$N=$$ N = 3.7 beds. Further, in the $$N=$$ N = 3.7 bed, channels of high velocity, termed flow lanes, were found 1.3 particle diameters from the wall, possibly due to the bed structure in this particular bed. At the pellet scale, the hydrodynamics were found to be independent of $$N$$ N . The results reported here demonstrate the capability of magnetic resonance velocity imaging for studying turbulent flows in packed beds, and they provide fundamental insight into the effect of $$N$$ N on the hydrodynamics.
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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 | 0.000 | 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".