Determining the Bingham yield stress and viscosity of fly‐ash slurries using mini‐cone slump tests: A numerical study
Bibliographic record
Abstract
Abstract The slump test was utilized to characterize the fluidity of slurries. Predicting the yield stress and viscosity of slurries based on this test remains a significant challenge. This study employs the dynamic meshing approach in Fluent software to simulate the influence of the lifting process of the mould during the slump test on the final deposition profiles of slumped slurries (PSS). The fly‐ash slurries were modelled as Bingham fluids in this study. The results indicate that at a lifting velocity of 0.01 m · s −1 , the impact of Bingham viscosity on the PSS is negligible. As the lifting velocity is 0.1 m · s −1 , the Bingham viscosity significantly affects the PSS. Zhang's approach that estimates Bingham yield stress from the spread diameter was modified. Regardless of the lifting velocity, the Bingham yield stress of the slurry exerts a remarkable influence on the PSS. These results demonstrate that conducting slump tests at two different lifting velocities enables reliable estimation of both Bingham viscosity and yield stress parameters for fly‐ash 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) | 0.000 | 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 | 0.001 | 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".