Simulation of mixing dynamics in agitated pulp stock chests using CFD
Bibliographic record
Abstract
Abstract Agitated‐pulp chests function as low‐pass filters to reduce high‐frequency variability in pulp properties (mass concentration, freeness, and so on) ahead of many pulping and papermaking operations. Tests on both industrial and scale‐model chests have shown that their dynamic performance is far from ideal, with a significant extent of nonideal flow (short circuiting, recirculation and stagnation) possible. The flow field of a 1:11 scale‐model pulp chest was modeled using a commercial computational fluid dynamics (CFD) software package (Fluent) with the pulp suspension treated as a modified Bingham plastic. A multiple reference frame approach was used with coupling between reference frames made using a velocity transformation. The flow profiles predicted by the simulation agreed qualitatively with those observed in the experiments. The power input predicted by the simulations was slightly higher (about 12%) than that measured. The velocity field obtained from the CFD model was used to obtain the system's dynamic response to a frequency‐modulated random binary input signal. These data were then used as input to a dynamic model that treated flow within the chest as following two streams: one bypassing the mixing zone and one entering it. For both streams, the fraction of suspension passing through each zone was determined and a time constant and delay time computed. These parameters were then compared to those measured experimentally under identical operating conditions. The CFD simulation provides detailed information on the velocity profile within the chest and allows the location(s) of poor mixing regions to be identified. © 2006 American Institute of Chemical Engineers AIChE J, 2006
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".