Design Optimization of a Bi-Fold Magnetorheological Damper Subject to Impact Loads
Bibliographic record
Abstract
Dynamic range (ratio of the maximum on-state damping force to the off-state damping force), is an important index characteristic of the performance of the Magnetorheological Energy Absorbers (MREAs). In high speed impact, the dynamic range may fall into the uncontrollable zone (≤ 1) due to the increase in the off-state damping force which is associated with the transition of the flow from laminar to turbulent condition. Therefore, it is of paramount importance to design optimize the MREA in order to increase its dynamic range while accommodating the geometry, MR fluid flow and magnetic field constraints. In this study, a design optimization problem has been formulated to optimally design a bi-fold MREA to comply with the helicopter crashworthiness specifications for lightweight civilian helicopters. It is required to have a minimum dynamic range of 2 at 5 m/s impact velocity while satisfying the constraints imposed due to the geometry, volume of the device, magnetic field and the flow of the magnetorheological fluid in the MR valve. Meanwhile in order to comply with the helicopter crashworthiness requirement, the MREA device should be designed to generate 15 kN field-off damping force at the design impact velocity if the MREA is to be integrated with skid landing gear systems. The magneto-static analysis of the MREA valve has been conducted analytically using simplified assumptions in order to obtain the relation between induced magnetic flux in the MR fluid gaps in active regions versus the applied current and MREA valve geometrical parameters. Both Bingham plastic models, with and without minor loss factors, have been utilized to derive the dynamic range and the results are compared in terms of the generated off-state damping force, on-state damping force, and dynamic range. The Bingham plastic model with minor loss coefficients was found to be more accurate due to the turbulent condition in the MREA caused by the impact. Finally, the performance of the optimized bi-fold MREA has been evaluated under different impact speeds.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".