Modelling and validation of a gas‐solid fluidized bed using advanced measurement techniques
Bibliographic record
Abstract
Abstract With a Euler‐two‐phase (E2P) approach, through computational fluid dynamics (CFD) techniques, a mathematical model for the prediction of the local hydrodynamic behaviour of a gas‐solid fluidized bed was implemented. Simulations are conducted for a fluidized bed of 0.14 m internal diameter packed with Gerdart B glass beads particles, with an average diameter of 365 μm, at dimensionless inlet velocities ranging from . The implemented model considers the multiphase and multiscale interactions through the inclusion of three sub‐models, which allows the model to have a broad range of applicability. Predictions were compared against experimental measurements reported on previous contributions for validation purposes. The experimental study was conducted by implementing advanced measurement techniques, such as a differential pressure transducer, and an optical fibre probe for simultaneous measurement of solids holdup and velocity, developed at the Multiphase Flow and Reactors Engineering and Applications Laboratory (mFReal). Local radial solids holdup, solids velocity, and pressure drop profiles were experimentally determined. Results show that the implemented model possesses a high predictive quality, predicting pressure drops with an average absolute relative error (AARE) between 8.6%–11.3%; solids holdup with a root mean squared deviation (RMSD) under 5%; and solids velocity with a RMSD under 22%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".