HEAT TRANSFER TO CANNED PARTICULATES IN HIGH-VISCOSITY NEWTONIAN FLUIDS DURING AGITATION PROCESSING
Bibliographic record
Abstract
Heat transfer to canned particulate-laden Newtonian high-viscous fluids (Nylon particles suspended in aqueous glycerin solution [40, 60, 80, 90 and 100%, v/v] and motor oil [85W140]) during end-over-end rotation was studied in a pilot-scale, full water-immersion single-cage rotary retort. Computations of conventional fluid-to-particle heat transfer coefficient (hfp) and overall heat transfer coefficient (U) were successful with multiple particles for an entire range of viscosity, but the predicted particle lethality was underestimated. With a single particle in the can, hfpand U calculations were successful only for low-viscosity fluids (40 and 60% glycerin solutions), but again resulted in underestimation of particle lethality. Apparent heat transfer coefficients (hap) between retort and particle surface and apparent overall heat transfer coefficient (Ua) were also evaluated, and this methodology worked well for all cases. Further, the particle lethality predicted using hap better matched the measured values. With a single particle in the can, the associated hap was significantly (P < 0.05) influenced by rotation speed, retort temperature, liquid viscosity, particle material and can size. Ua was significantly (P < 0.05) influenced by rotation speed and liquid viscosity. The effects of headspace, radius of rotation and particle size were not significant (P > 0.05) on hap and Ua values.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".