Embedded Fiber Optic Sensors for High Strain Composite Components
Bibliographic record
Abstract
Helicopter rotor systems are increasingly using flexible composite structures to provide the required control movement for the rotor blades. These structures such as rotor head flex beams can experience very high surface strains, which can be in the order of 15,000 με. This makes it difficult for them to be monitored using conventional surface bonded strain sensors. Helicopter rotor hubs incorporating thick composite flexures are subjected to delamination failures with correlate to bending excursions. Measurement of this deflection could be used to predict remaining useful life of the flexure and other hub components. Previous efforts to measure surface strains using fiber optic sensors led to mixed results and prompted an effort to explore embedded sensors. As part of a Vertical Lift Consortium project, the authors tested proof of concept manufacturing specimens to establish the ability of the sensors to survive the cure process in a closed cavity mold tool. Multiple fibers were evaluated based on their post-cure properties. Using the selected fiber type, the team designed and built full scale flexure elements both with and without embedded sensors to evaluate the ability of the fibers to accurately correlate flexure strain to bending angle. Testing also established the effectiveness of temperature compensation using a strain-free reference sensor. Finally, fatigue testing of elements both with and without embedded sensors established that the embedded sensors did not adversely affect the laminate durability. Post test investigation showed the delamination failures did not initiate at embedded sensor locations. It was therefore concluded that the embedding of fiber optic strain sensors are a possible solution to measure strains in helicopter rotor components.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".