Comparison between numerical results and PIV experimental data for gas–solid flow in ducts
Bibliographic record
Abstract
To analyse the behaviour of gas–solid flow, the experimental, nonintrusive technique of particle image velocimetry (PIV) was applied. This technique enables the acquisition of information regarding the microscopic velocity field in a bidimensional plane. The physical experiments were conducted in the vertical and horizontal sections of a test facility. The operating conditions at the inlet were 140 m3/h of air and a 40 g/m3 mass load ratio, which are typical conditions for dilute flows. Solid‐phase catalyst particles with a Sauter mean diameter of 56.7 µm, similar to those applied in the petroleum industry for FCC systems, were used. Experimental radial profiles for the axial velocity data of the solid phase were compared with the respective numerical results obtained by the CFD code in FLUENT 13. Turbulence in the gas phase was modelled with a k − ϵ model, and second‐order versions of this model were used for turbulence in the solid phase. Turbulence was induced by the drag force with direct numerical simulation (inviscid model) and was modelled using the kinetic theory for granular material with the equilibrium model (KTGF equilibrium). The results showed that both the inviscid and KTGF models produced good agreement with the experimental data for dilute gas–solid flow in ducts, particularly in regions of developed flow.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".