Flow of solids in an interconnected fluidized beds system investigated using positron emission particle tracking
Bibliographic record
Abstract
Abstract The technique of Positron Emission Particle Tracking (PEPT) is applied to analyse non‐intrusively solids flow in a laboratory scale Interconnected Fluidized Beds system. It is shown that, with a high performance tracking system, it is possible to extract from the data not only the Eulerian ensemble average velocity field, but also to reconstruct density profiles and to calculate mass flows in a solids circulation system. The most interesting flow characteristics inside the Interconnected Fluidized Bed system are highlighted. Although the data analysis is largely Eulerian, a first approach to a Lagrangian analysis has been performed through the computation of velocity autocorrelation functions. Characteristic frequencies of the solids movement could not be identified, but the velocity autocorrelations did provide insight in the dynamics of the solids movement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".