Experimental Analysis of the Mechanical and Thermal Behaviour of Epoxy and Polyester GFRP Composites for Wind Turbine Blade Skin
Bibliographic record
Abstract
This study evaluates the mechanical performance and thermal stability of glass fiber reinforced polymer (GFRP) with polyester and epoxy resin as polymer matrix for flexible skin applications in wind turbine blades.The composites were fabricated using the Vacuum Assisted Resin Transfer Moulding (VARTM) technique and tested through twist-tensile (ASTM D638) at angles of 0°, 20°, 40°, and 60°.Three-point bending tests (ASTM D790) were performed using 60 mm and 96 mm span configurations.The tensile test results revealed that GFRP-epoxy achieved a maximum strength of 334 MPa (0°), surpassing that GFRP-polyester, which exhibited reduced performance due to less optimal interfacial bonding.In the bending tests, GFRP-epoxy demonstrated superior performance with a strength of 519 MPa, while GFRP-polyester showed a significant reduction, particularly in the short span bending.Failure modes analysis using SEM revealed that GFRP-epoxy attained good fiber wetting and strong interfacial bonding.The GFRP-polyester composites were dominated by fiber pull-out, delamination, and debonding.The thermal stability of both is in the range 300℃ to 340℃.These findings confirm that GFRP-epoxy composite is more suitable as the primary matrix for a Horizontal Axis Wind Turbine (HAWT) blade due to its superior structural and thermal stability.In contrast, GFRP-polyester laminated composite is more appropriate for nonstructural components with lower load demands.
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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".