Active Control of Seats to Reduce Helicopter Aircrew Exposure to Vibration
Bibliographic record
Abstract
Undesired vibration transmitted through the helicopter seats have been known to cause fatigue and discomfort to the aircrew in the short-term as well as neck strain and back pain injuries due to long-term exposure. This research study investigated the use of active control technologies integrated in helicopter seat structure to mitigate the vibration transmitted to the aircrew. As a pre-requisite to develop an effective active seat structure, a flight test was conducted to acquire the aircrew and structural vibration data using the NRC Bell-412 aircraft under representative flight conditions. The data acquired during the flight test clearly concluded the necessity to reduce the level of vibration experienced by Bell-412 helicopter aircrew. An active seat structure based on smart structure concept was developed using two stacked piezoelectric actuators with a real-time feed forward controller to counteract the forced excitation. Closed-loop control experiments performed using a full-scale seat excited using a mechanical showed significant vibration reduction. Test results concluded that the proposed active seat structure based on smart structures technology is a viable solution to reduce vibration exposure of helicopter aircrew to mitigate fatigue and vibration induced health issues.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".