Development, optimization, and control of a novel magnetorheological brake with no zero-field viscous torque for automotive applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The significant energy loss due to viscous torque generation in the absence of the applied magnetic field is the main obstacle in the practical realization of magnetorheological brake in the automotive applications. In this study, a novel magnetorheological brake design having no viscous torque generation in the absence of applied magnetic field has been proposed. The Herschel–Bulkley constitutive model is employed to develop the mathematical equations governing the system’s braking torque. Magnetic circuit analysis of the proposed magnetorheological brake has been conducted to predict the magnetic field intensity in the magnetorheological fluid gaps. A multidisciplinary optimization problem has been formulated to identify the optimal brake parameters to maximize the braking toque while minimizing response time and weight of the magnetorheological brake under different constraints. Genetic algorithm combined with sequential quadratic programming algorithm has been utilized to find the true global optimal solution. The optimal design of the proposed magnetorheological brake provides a maximum braking torque of 1802 N m, a response time of 150 ms, and an overall weight just under 37 kg. Finally, braking performance of the proposed magnetorheological brake has been investigated in a quarter vehicle model where a proportional–integral–derivative controller has been integrated with the proposed magnetorheological brake to improve vehicle’s slipping on different road conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it