CFD modeling and simulation of clogging in packed beds with nonaqueous media
Bibliographic record
Abstract
Abstract When liquids containing low concentrations of fine solid impurities are treated in packed‐bed reactors, clogging develops and starts hampering the flow severely. This phenomenon, called deep‐bed filtration, constitutes serious concern over hydrotreating or hydrocracking of bituminous sands in packed‐bed reactors, in which such nonfilterable fines as native clay or incipient coke cause reactor dysfunction by clogging. A detailed k‐fluid Eulerian 2‐D transient computational‐fluid dynamic (CFD) model was formulated to describe the space‐time evolution of clogging patterns developing in deep‐bed filtration of the liquids. A local formulation of the macroscopic logarithmic filtration law is proposed, as well as a geometrical model for the effective specific surface area of momentum exchange. Both mono‐ and multiple‐layer deposition mechanisms were accounted for by including appropriate filter coefficient formulations. Transient, 2‐D axisymmetrical simulations were benchmarked using experimental results and observations of Narayan et al. (1997) of the carbon‐black contaminated kerosene flow through packed beds. Comparing the simulations and experiments showed that CFD is useful for the quantitative description of packed‐bed clogging.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".