Hydrodynamic Profiles Of Computed Tomography-Scanned Polydispersed Beds Produced By Sieving
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) models are a valuable tool for design, optimization, and scaling-up of fixed bed chemical reactors.However, the realistic representation of the catalytic bed structure and the mesh quality of the 3D geometry is of paramount importance to improve the accuracy of CFD models.For the former, computed tomography (CT) is a non-destructive method to map and generate the internal structure of actual fixed bed reactors, formed by catalytic particles produced by sieving, thus directly coupling experiments with CFD models.Due to the local topological complexity of these beds, however, meshing their entire volume would lead to exhaustive computational demands.To reduce these, a suitable sample section should be selected, which respects the bulk and radial porosity of the full bed as accurately as possible.Three distinct sample sections were quantified here for their accuracy, identifying that, due to the highly heterogeneous nature of the full beds, sample selection is case sensitive.A selected section was then meshed, and its hydrodynamic profile resolved, to evaluate its mesh independency.The results highlight the importance of choosing a suitable bed section and mesh size to reduce the computational demands, minimise the computational errors, and achieve the desired level of solution detail.
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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".