Modelling 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
Abstract Epoxy resins are essential for the manufacturing of GFRP/XPS foam sandwich structures used for hydraulic turbine extension stay vanes. Their properties during and after curing are key factors for the performance of the entire hybrid composite structure. This paper introduces experimental characterization and modeling of the influence of the quantity and size of ATH fillers on the curing and post-curing characteristics of the epoxy resin. The experimental investigation involves the maximum temperature, polymerization time, shrinkage, viscosity, and flexural properties. The mass fractions of the ATH were 10, 20, 30, 40, 50, and 60%, and the particle sizes were 2, 4, 6, 8, and 12 µm. In addition, we utilized the multivariate polynomial regression (MPR) and artificial neural network (ANN) methods to develop empirical models to predict the maximum temperature, polymerization time, shrinkage, and flexural modulus. The experimental results showed that increasing ATH mass fraction with smaller particle size delayed polymerization and lowered the maximum temperature. The experimental viscosity values showed that Mooney model can accurately calculate viscosity as a function of ATH mass fraction and particle size, compared to the Quemada and Krieger-Dougherty models. Adding ATH increased flexural strength, modulus, and breakage strain. The developed models achieved a higher than 0.9 correlation coefficient between the predicted and measured responses and can be used to enhance the design and control the 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) | 0.000 | 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 | 0.000 |
| 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".