Modeling of the effect of ATH fillers on the rheology, curing kinetics, and flexural properties of the epoxy resin forming the hydraulic turbines’ stay vanes extension
Bibliographic record
Abstract
Epoxy resins are crucial for the production of GFRP/XPS foam sandwich structures used for hydraulic turbine extension stay vanes. The quantity and size of ATH fillers have a significant impact on the curing and post-curing characteristics of the epoxy resin. This paper presents the results of an experimental study of the effect of ATH fillers on the maximum temperature, polymerization time, shrinkage, viscosity, and flexural properties of the epoxy resin. The study also uses regression and neural network methods to develop models to predict these properties based on ATH mass fraction and particle size. The results showed that increasing the mass fraction of ATH with a smaller particle size delayed polymerization and reduced the maximum temperature. The addition of ATH resulted in an improvement of the flexural modulus; nevertheless, it caused a reduction in both the flexural strength and breakage strain. Adding ATH improved flexural strength, modulus, and breakage strain. The models developed in this study had a high correlation between predicted and measured responses, providing valuable information for the design and casting of the proposed sandwich structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".