Dynamic chaos of imaging measurements for characterizing gas–liquid nonlinear flow behaviour in a metallurgical reactor stirred by top‐blown air
Bibliographic record
Abstract
Abstract A modern chaos detection strategy for imaging measurements is proposed in this work to quantify the thermal mixing state quality in a metallurgical reactor stirred by top‐blown air. Specifically, the improved C‐C algorithm is proposed to reconstruct the phase space of the nonlinear time series of the bubble characteristics under thermal conditions. Moreover, a chaos decision tree algorithm is introduced to extract the chaotic mixing characteristics of the thermal two‐phase mixing system for the first time. Experimental and calculated results show that all the possible mixing states of the mixing system are visualized by reconstructing the phase space with the help of the visualization technique of the thermal gas–liquid two‐phase flow. It is found that the attractor of the nonlinear time series of bubbles exhibited more serious variations while the chosen working condition was optimal. Furthermore, the obtained parameter represents the chaotic characteristics of the thermal gas–liquid two‐phase mixing system which has chaotic characteristics under various experimental conditions. Hence, the new strategy would be helpful and effective in beneficial exploration for understanding the nonlinear intensification mechanism of metallurgical thermal processes.
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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 | 0.001 |
| 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 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".