Bibliographic record
Abstract
Modern articulated rotors utilize an elastomeric bearing on the root of each blade to allow multi-axis articulation through a single component. The bearing forms a spherical joint that makes measuring angular displacements in specific blade axes both coupled and difficult to measure. A smart sensing concept is developed to uncouple and separately measure the blade flapping, feathering, and lead-lag position. The sensor concept projects a magnetic field from the blade root to the rotor hub such that flap, lag and pitch displacements can be derived from a hub mounted pickup array. The device utilizes permanent magnets on the blade root and an array of Anisotropic Magnetoresitive (AMR) sensors on the hub. The sensor array is integrated with other electronic components, including a microprocessor in a printed circuit board, forming a self contained sensing system. The 'smart' sensor is software driven, self checking, and can be networked with other sensors for application flexibility. The sensor accuracy is ensured through a process that develops calibration coefficients that are loaded into system non-volatile memory. The sensor system is designed for initial application on an articulated tail rotor for flight test purposes. Risk reduction testing is conducted in simulated tail rotor environmental conditions including vibration and shock, elevated centrifugal force (CF), temperature extremes and electromagnetic emissions / susceptibility. All development and test results indicate the new sensor technology is valid and ready for use in its initial flight test application.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".