Performance test and finite element modeling of variable damping viscous damper
Bibliographic record
Abstract
The application of traditional fluid viscous dampers was constrained by their typically constant damping coefficient , which produced a narrow range of damping force . To address this, this paper develops a novel variable damping viscous damper (VDVD) with the feature of variable damping coefficient and force in the passive control domain. The performance tests of VDVD under various loading velocities were carried out to investigate the variable damping characteristic. Simultaneously, tests were conducted to investigate the variable damping characteristics of two critical design parameters: the pre-pressure force of the spring and the shape of the damping orifice. Furthermore, the finite element (FE) model of the device was built using computational fluid dynamics (CFD) method, and verification and parameter analysis were conducted. Through experimental study and finite element simulations , the design philosophy of VDVD was verified. Both experimental and FE analysis show that the shape of damping orifice and pre-pressure of spring will significantly affect the process and critical velocity of variable damping respectively. The residual area and shape of the damping orifice impact the magnitude of the damping force directly. This study offers a thorough comprehension for the design of VDVD which will promote the development of variable damping devices.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".