Development of an Automotive Magnetorheological Brake via Design Optimization of the Magnetic Circuit
Bibliographic record
Abstract
The design, analysis and optimization of a novel electromechanical brake for automotive applications is presented . The proposed brake consists of multiple rotating disks immersed in a magnetorheological (MR) fluid, and an enclosed electromagnet. When current is applied to the electromagnet, the MR fluid solidifies as its yield stress varies as a function of the magnetic field applied. This controllable yield stress produces shear friction on the rotating disks, generating the braking torque. An electromagnetic finite element analysis was performed to optimize the magnetic circuit within the MR brake in order to obtain the optimal design parameters that maximize the braking torque generated. Finally, a prototype MR brake was built and tested and the experimental results showed good correlation with the the finite element simulation predictions. Nomenclature A Cross-sectional area of medium [m 2 ] dbrake Outer diameter of MRB [m] h MR fluid gap thickness [m] Hcore Magnetic field intensity on magnet core [A/m] Hdisk Magnetic field intensity on disk section [A/m] HMRF Magnetic field intensity on MRF [A/m] i Current applied to the coil [A]
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".