Preliminary Results on the Effect of Methanol-Based Fuels on the Tensile Properties of Frp Micro-Specimens
Bibliographic record
Abstract
This paper reports a simple experimental technique developed to measure strains in fibre-reinforced plastic (FRP) tensile micro-specimens (nominal thickness: 254 μm) before and after these were exposed to a 50–50 volumetric mixture of methanol and ASTM Fuel C. Micro-specimens were used to reduce the time required for the fuel mixture to diffuse into the FRP. The developed technique is then used to study the effect of the methanol-based fuel on the tensile properties of the micro-specimens. In particular, the stress of the specimens at a strain of 1.5% is seen to be significantly lower when the specimens are tested immediately after exposure to the fuel for 3 day and 7 day periods as compared to the stress for specimens not exposed to the fuel. The loss in stress is found to be significantly recoverable when the exposed specimens are tested after allowing them to dry for an equal length of time, i.e. 3 days and 7 days. These results point to two possibilities: 1. Design of FRP structures exposed to alcohol-based fuels, e.g. underground fuel storage tanks, may have to account for noticeable mechanical property changes of the FRP during the service period, 2. Any property changes may be partially reversed by allowing the structures to “dry” over an appropriate period of time.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".