A comprehensive approach for optimal design of magnetorheological dampers
Bibliographic record
Abstract
Magnetorheological dampers have been used in automotive industry and civil engineering applications for shock and vibration control for some time. While such devices are known to provide reliable shock and vibration suppression, there exist emerging applications in which the magnetorheological dampers have to be optimized in terms of power consumption and overall weight (e.g. energy-efficient electric vehicles). Utilizing traditional optimal design approaches to tackle those issues can sometimes lead to convergence problems such as getting trapped in a local extremum and failing to converge to the global optimum. Furthermore, manufacturing limitations are usually not taken into account in the optimization process which may hamper achieving an optimal design. In this article, we present a method for optimal design of magnetorheological dampers by utilizing mathematical optimization and finite element analysis. The proposed method avoids infeasible solutions by considering physical constraints such as fabrication limitations and tolerances. This approach takes every single feasible solution into account so that the final solution would be the global extremum of the optimization cost function. The proposed approach is applied to optimize a complex magnetorheological damper structure with different types of materials such as steel and AlNiCo. In particular, we present the design of a valve-mode magnetorheological damper with AlNiCo integrated as its core. A magnetorheological damper prototype is manufactured based on the proposed optimization method and tested experimentally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".