An experimental investigation of fluid dynamics and non‐uniformity of fluidization in dense gas–solids fluidized bed with chaos analysis
Bibliographic record
Abstract
Abstract As industrial‐scale dense gas–solid fluidized bed separators have been gradually employed in dry coal beneficiation, it is urgent to develop feasible and efficient methods to evaluate and quantify the bed density stability, which directly influences the coal separation efficiency. The instantaneous signals recorded with the pressure sensor and optical fibre probe (OFP) is utilized to analyze the nonlinear characteristics and evaluate the complexity and instability of the fluidization process for dense gas solid fluidization. It is verified that the hidden chaotic characteristics of pressure drop signals can be retrieved with multi‐dimensional reconstruction of attractor. Based on the reconstructed attractor, the Shannon entropy and Kolmogorov entropy are investigated and estimated under different bed heights and air flows. The results indicate that the differential pressure signal and the optical fibre signal are typical chaotic signals, effectively representing the complexity of fluid dynamics in the local measurement space. Nonlinear bubble behaviour is the primary cause of the increased rate of information loss in chaotic signals, which severely affects the stability of fluidization quality in the bed. The spatial distribution of the chaos index corroborates the internal circulation pattern within the fluidized bed, which can feasibly characterize and quantify the nonlinear characteristics and instability of gas solid fluidization.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".