Development of the Smart Spring for Active Vibration Control of Helicopter Blades
Bibliographic record
Abstract
Significant structural vibration is an undesirable characteristic in helicopter flight that leads to structural fatigue, poor ride quality for passengers and high acoustic signature for the vehicle. Previous Individual Blade Control (IBC) techniques based on piezoelectric actuator schemes to reduce these effects have been hindered by electromechanical limitations of piezoelectric actuators. The Smart Spring is an active tunable vibration absorber using the IBC approach to adaptively alter the “structural impedance” at the blade root. In the paper, a mathematical model was developed to determine the response of the absorber under harmonic excitation. An adaptive notch algorithm using a DSP platform was developed to implement vibration control. Reference signal synthesis techniques were used to automatically track the shift in the fundamental vibratory frequency due to variations in flight conditions. Experiments using a mechanical shaker and wind tunnel tests conducted on the proof-of-concept hardware achieved significant vibration suppression at harmonic peaks. Investigation verified the capability of the Smart Spring to suppress multiple harmonic components in rotor vibration through active impedance control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".