CFD analysis of magnetorheological fluid clutch
Bibliographic record
Abstract
Magnetorheological (MR) fluid is a type of smart fluid, in which magnetic particles are suspended in a non-magnetic carrier liquid, such as silicone oil. MR fluid is versatile, and caused a significant advancement in actuator technology. When subjected to an external magnetic field, MR fluids exhibit significant and reversible changes in their rheological properties, offering superior control compared to traditional hydraulic actuators while being lighter and more cost-effective. The MR fluid (MRF) clutch is one of the most essential types of actuators for torque transmission in rotating systems. Its reliability stems from the absence of mechanical contacts, which minimizes wear and enhances durability. Most research on MRF clutches has focused on their physical principles and optimization, and often using experimental setups. However, detailed investigations into MR fluid behavior within complex clutch geometries, particularly at high particle volume fractions, remain limited. This study uses a finite-volume based solver and solves the conservative equations for a single-phase highly concentrated MR fluid, to simplify the modeling. The numerical model shows first an excellent agreement with experiments in terms of torque. The influence of the magnetic flux density on the MRF’s dynamic viscosity and velocity profiles is then quantified up to 0.5 T. The magnetic field drastically affects both the fluid flow and properties by inducing large spatial variations in the small rotor-stator gaps.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".