Design Optimization of a Magneto-Rheological Fluid Brake for Vehicle Applications
Bibliographic record
Abstract
The magnetorheological Brake (MRB) is an electromechanical brake in which smart magnetorheological (MR) fluids have been utilized to generate the required braking torque. The purpose of this study is to design optimize a real-size MRB for automobile applications considering geometrical, material and magnetic circuit parameters. The mathematical equations governing the system’s braking torques are derived. The dynamic range of a disk-type MRB expressing the ratio of generated toque at on and off states has been formulated as a function of the rotational speed, geometrical and material properties, and applied electrical current. The magnetic circuit analysis of the proposed MRB is performed to find the relation between magnetic field intensity and the applied electrical current as a function of the MRB geometrical and material properties. Finally, a multidisciplinary design optimization problem has been formulated to identify the optimal brake geometrical parameters to maximize the dynamic range of the MRB under weight, size and magnetic flux density constraints. The optimization problem has been solved using combined Genetic Algorithm and Sequential Quadratic Programming techniques. The optimal design is then compared with those available in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".