Development of a Multi-Axis Active Seat Mount System for Helicopter Aircrew Whole-Body Vibration Mitigation
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-1835.vid This paper presents the development and evaluation of an actively controlled multi-axis helicopter seat mount system for aircrew whole-body vibration mitigation applications. The multi-axis seat mount system is designed to be installed between the helicopter seat floor and the supporting structure of seat frame to minimize the impact on the crashworthiness requirements of the helicopter seat. The active seat mount was designed to incorporate multiple miniature force actuators to counteract the vibrations transmitted from the helicopter floor to the seat frame and aircrew in three orthogonal directions. The actuators are controlled by an adaptive feedforward filtered-x Least Mean Square (LMS) algorithm to cancel the helicopter floor vibration input. The prototype active seat mount design has been tested with a Bell-412 pilot seat and three Hybrid III manikins, with a shaker table to provide representative Bell-412 helicopter vibration profiles. Test results demonstrated that the vibrations of the seat frame and manikin occupant were suppressed simultaneously, and the occupant whole-body vibration related to the major N/rev harmonic peaks were reduced by 90%. This demonstrated that the multi-axis active seat mount design has the potential to mitigate the whole-body vibration exposure of the helicopter aircrew to improve their ride quality and reduce vibration related adverse health effects.
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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 | 0.000 |
| 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) | 0.006 | 0.002 |
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".