Rheology of ethylene- and propylene-glycol ice slurries: Experiments and ANN model
Bibliographic record
Abstract
An experimental investigation combined with a numerical study is performed to characterize the rheological behavior of ice slurries. Two additives, namely ethylene glycol and propylene glycol, are considered at three initial concentrations X a = 5, 14 and 24%. The ice fraction is varied from 5 to 65%. Flow ramp tests are carried out using a hybrid HR-2 rheometer . The Herschel–Bulkley model is then employed to predict the rheological behavior of ice. Using a least-square approach, the flow index n and the consistency index k are deduced from the rheograms. The ice slurries exhibit either a shear-thinning or a shear-thickening behavior depending on the operating conditions. An experimental database is produced based on the present experiments and on experimental data retrieved from the literature. An Artificial Neural Network (ANN) model is then developed and validated using this database and appears to be a valuable tool for predicting the rheological behavior of ice slurries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".