Effect of turbulent fluctuation on the ignition of millimeter particle: Experimental studies and numerical modelling with a new correlation of nusselt number
Bibliographic record
Abstract
Understanding the influencing mechanism of turbulent fluctuation on the ignition characteristics of millimeter coal particles is essential. In this work, to study the effect of turbulent fluctuation on ignition time , millimeter coal particles are subjected to a specific flow field, generated in a furnace with symmetric fans. A one-dimensional model with the new proposed correlation and the Ranz-Marshall (R-M) correlation for Nu (Nusselt number) is established to simulate the coal ignition process. In addition, the effects of fan speed, temperature, particle diameter, particle distance and coal type on the ignition time are investigated. It is found that an increase in fan speed from 0 to 3000 rpm leads to a particle Reynolds number Re p increase from 0 to 22.5, and a turbulent particle Reynolds number Re t ∗ increase from 0 to 71.5. With a consideration of the fluctuation effect, the new correlation of Nu gives a better prediction of ignition time compared to the R-M correlation. Moreover, the ignition time is revealed to decrease with an increasing fan speed and an elevating temperature. While the ignition time shows merely an initial boost with enlarging particle distance, it exhibits a linearity with the term of particle diameter d p 1.3–1.7 and Reynolds numbers ( Nu∗/Nu ) –0.6 ( Nu∗ is turbulent Nusselt number). Based on this relationship, the difference of predicted ignition time is calculated at different Re p and Re t ∗. It is shown that at low Re p or high Re t ∗ values, the new correlation should substitute for the R-M correlation.
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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".