Dynamic analysis of electro- and magneto-rheological fluid dampers using duct flow models
Bibliographic record
Abstract
Magneto-rheological (MR) and electro-rheological (ER) fluid dampers provide a semi-active control mechanism for suppressing vibration responses of a structure. MR and ER fluids change their viscosity under the influence of magnetic and electrical fields, respectively, which facilitates automatic control when these fluids are used in damping devices. The existing models, namely the phenomenological models for simulating the behavior of MR and ER dampers, rely on various parameters determined experimentally by the manufacturers for each damper configuration. It is of interest to develop mechanistic models of these dampers which can be applied to various configurations so that their fundamental characteristics can be studied to develop flexible design solutions for smart structures. This paper presents a formulation for dynamic analysis of electro-rheological (ER) and magneto-rheological (MR) fluid dampers in flow and mix mode configurations under harmonic and random excitations. The procedure employs the vorticity transport equation and the regularization function to deal with the unsteady flow and nonlinear behavior of ER/MR fluid in general motion. The finite difference method has been used to solve the governing differential equations. Using the developed approach, the damping force of ER/MR dampers can be calculated under any type of excitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".