Magnetic resonance velocity imaging of turbulent gas flow in a packed bed of catalyst support pellets
Bibliographic record
Abstract
Compressed sensing magnetic resonance methods have been used to image the time-averaged velocity and turbulent kinetic energy in 3D for turbulent gas flowing through a bed of porous, hollow cylindrical catalyst support pellets. Velocity and turbulent kinetic energy images were acquired at a spatial resolution of 0.70 mm ( x ) × 0.70 mm ( y ) × 1.0 mm ( z ) for particle Reynolds numbers, R e p , of 500, 2500 and 6500 in a bed with a tube-to-particle diameter ratio of 4.7. These data represent the first full-field measurements of turbulent gas flow in packed beds of non-spherical pellets. The resulting images reveal several interesting features of the hydrodynamics in this system. A large degree of flow heterogeneity is observed in the bed, with regions of high-speed fluid observed near the walls and in large voids, and regions of backflow observed in the wake of pellets, between pellets, and within the pellet holes. For increasing R e p , the normalized axial velocity at the wall is found to increase, and the normalized turbulent kinetic energy becomes more homogeneous throughout the bed. The correlation between the turbulent kinetic energy and time-averaged velocity shows that the highest turbulent kinetic energy occurs in regions of intermediate time-averaged velocity. Further, the turbulent kinetic energy profile at the pellet-scale is substantially different from the case of simple channel flow for R e p ≥ 2500. Overall, these measurements clearly demonstrate the ability of magnetic resonance methods for acquiring full-field flow data in packed bed systems using commercially-relevant pellets and flow conditions.
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How this classification was reachedexpand
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)
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 teacher head, 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".