Numerical and Experimental Investigation of a Single-Axis Parallel Active Vibration Mount for Helicopter Seats Using Vibration Standards
Bibliographic record
Abstract
Abstract This paper presents the experimental and numerical investigation of a single-axis replicate of a patented multi-axis active vibration isolation seat mount. Following the design of the multi-axis system, this single axis vibration isolation mount uses a flexible elastomer support placed in parallel with an electromagnetic actuator. This mount is designed to reduce the N/rev harmonic vibration of a helicopter using a filtered-X least mean square (FXLMS)-based controller. To improve the efficiency of the FXLMS controller for this application, the ISO-2631-1 Wk filter is added. Employing this modified controller, the experimental setup is tested using a payload mass representative of a 95th percentile pilot. The experimental results confirm the effectiveness of the proposed design in canceling the unwanted helicopter vibration, where the active mount effectively reduces the vibration representative of a Bell-412 helicopter by 69.37% (−10.28 dB, g-rms). In order to develop a better understanding of the problem, the system is also modeled from first principles in simulink. The comparison between the nonlinear numerical model and the experimental results demonstrates a good agreement between the two approaches. Moreover, it is shown that the addition of the ISO-2631-1 Wk filter improves the transient performance of the FXLMS controller for the given helicopter vibration profile.
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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) | 0.000 | 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 | 0.000 | 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".