DIMENSIONLESS CORRELATIONS FOR CONVECTIVE HEAT TRANSFER IN CANNED PARTICULATE FLUIDS UNDER AXIAL ROTATION PROCESSING
Bibliographic record
Abstract
ABSTRACT Dimensionless correlations for estimating heat transfer coefficients (U and hfp) in canned high viscosity Newtonian liquids (with and without particles) were developed using stepwise multiple nonlinear regressions of statistically significant dimensionless groups using tangent as an estimate and Newton as search method. Data on overall heat transfer coefficient U and fluid‐to‐particle heat transfer coefficients hfpwere obtained for several processing conditions and were analyzed separately for particle and particle‐free conditions. In free axial mode, a newly developed form, combining natural and forced convection, provided higher R2 = 0.93. In the absence of particles in end‐over‐end mode, introducing natural convection term (Gr × Pr), improved R2 from 0.81 to 0.97. Combination of the reel radius, radius of the can and radius of the particles was chosen as characteristics length. PRACTICAL APPLICATIONS Most earlier dimensionless correlations for a canned liquid particulate mixture subjected to free axial mode of agitation are present for either U or hfp individually due to the difficulties in obtaining time–temperature profiles of the liquid and particles simultaneously; however, the time–temperature prediction at the particle center requires appropriate correlations for both U and hfp and cannot be made with only one of these coefficients. Additionally, importance of natural convection in forced convection heat transfer correlations has been demonstrated by developing the U and hfp correlations using the mixed convection approach as the combination of natural and forced convection heat transfer. These developed correlations would help in modeling the time–temperature profiles of a canned particulate mixture and will be helpful in determining the contribution of natural and forced convection heat transfer. These dimensional numbers would give a better understanding of the physical phenomenon and can also be easily used for scale‐up purposes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".