Numerical analysis of hydrodynamic characteristics and interphase coupling in a gas‐solid cyclone reactor
Bibliographic record
Abstract
Abstract A down‐flow gas‐solid cyclone reactor (GSCR) for fluidized catalytic cracking process was proposed to solve the side reactions. Comprehensive three‐dimensional numerical models were established to interpret its gas‐solid flow features, where the continuous phase and dispersed phase were modelled in the Eulerian grid and Lagrangian framework, respectively. Simulation results show that the four‐way approach has a better prediction of gas‐solid flow hydrodynamics in GSCR than the two‐way approach, and the numerical pressure drop agreed well with the experimental data. Most catalysts accumulated on the wall when descending and the phase interface structure composed of internal pure gas and external mixtures was subsequently formed. The radial distribution of tangential velocity was almost hump shaped. Tangential and axial kinetic energies accounted for 92.49%–95.45% of total kinetic energy and were thus dominant. In the dilute condition, the oscillatory behaviour of particle concentration was more severe than that of gas velocities, and particle concentration and gas tangential velocity had the same dominant frequency (~2.6 Hz). As the particulate loading increased tenfold, the hydrodynamic characteristics became rather complex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".