Application of fiber optic sensors for elevated temperature testing of polymer matrix composite materials
Bibliographic record
Abstract
Abstract Advanced polymer matrix composite (PMC) materials have been more frequently employed for aerospace applications due to their light weight and high strength. Fiber-reinforced PMC materials are also being considered as potential candidates for elevated temperature applications such as supersonic vehicle airframes and propulsion system components. A new generation of high glass-transition temperature polymers has enabled this development to materialize. Clearly, there is a requirement to better understand the mechanical behaviour of this class of composite materials. In this study, polyimide-coated fiber optic sensors are employed to continuously monitor strain in a woven carbon fiber bismaleimide (BMI) matrix laminate subjected to tensile static and fatigue loading at elevated temperatures. A unique experimental test protocol is utilized to investigate the capability of the optical sensors to monitor strain and track stiffness degradation of the composite material. An advanced interrogation system and an optical spectrum analyzer are utilized to track the variation in the optical fiber wavelength and the wavelength spectrum for correlation with strain gage measurements. Isothermal tensile static and fatigue tests at room temperature, 105°C, 160°C and 205°C suggest that these optical sensors are capable of continuously monitoring strain and tracking the stiffness loss of a highly compliant PMC specimen during cyclic loading. The results illustrate that employing optical sensors for elevated temperature applications has significant advantages when compared to conventional strain gages.
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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".