Scaling magneto-rheology based on Newtonian and non-Newtonian host fluids
Bibliographic record
Abstract
Abstract Rheology of a suspension is mainly determined by particle interactions and the host fluid rheology. By scaling rheological properties of different magnetorheological (MR) suspensions prepared with Newtonian and non-Newtonian (including shear thickening and shear thinning) host fluids, the competition between the particle interaction and the host fluid rheology could be clearly revealed. A simple normalizing method by considering the ratio of the magnetic force to viscous force experienced by particles is introduced. The shear stress curves of the three kinds of MR suspensions could be well scaled into the same master curve. The magnetic force maintains the particles structures under magnetic fields, while the viscous force is a destructive factor to the particle structures. Therefore, the magnetic force dominates the rheology of MR suspensions at low shear rates with small viscous force. While as the increase of shear rate to a very high value, particle chain structures under external magnetic fields might get similar to that under zero magnetic field because of the dominant role of the viscous force. This work clearly demonstrates the competition between the particle interaction and the viscous force experienced by particles, which finally dominates the particle structure inside of the MR suspension and its rheological behavior.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".