Squeeze-strengthening of magnetorheological fluids (part 1): Effect of geometry and fluid composition
Bibliographic record
Abstract
Recent research has shown that magnetorheological fluid can undergo squeeze-strengthening when flow conditions promote filtration. While a Péclet number has been used to predict filtration in non-magnetic two-phase fluids submitted to slow compression, the approach has yet to be adapted to magnetorheological fluid behavior in order to predict the conditions leading to squeeze-strengthening behavior of magnetorheological fluid. In this article, a Péclet number is derived and adapted to the Bingham rheological model. This Péclet number is then compared to the experimental occurrence of squeeze-strengthening behavior obtained from several squeeze geometries and magnetorheological fluid compositions submitted to pure-squeeze conditions. Results show that the Péclet number well predicts the occurrence of squeeze-strengthening behavior in high-concentration magnetorheological fluid made from various particle sizes and using various squeeze geometries. Moreover, it is shown that squeeze-strengthening occurrence is increased when using annulus geometries or by increasing average particle radius. While lowering concentration increases filtration, tested conditions only led to squeeze-strengthening behavior after concentration had increased close to packing limit. Altogether, results suggest that the Péclet number derived in this study can be used to predict the occurrence of squeeze-strengthening for various magnetorheological fluids and squeeze geometries using the well-known rheological properties of magnetorheological fluids.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".