Investigation of a Non-Equilibrium Energy Model for Resin Transfer Molding Simulations
Bibliographic record
Abstract
Due to the high design freedom and weight specific properties carbon fiber reinforced plastics (CFRP) offer significant potential in light-weighting applications, specifically in the automotive sector. The demand for medium to high production quantities with consistent material properties has paved the way for the use of high-pressure resin transfer molding (HP-RTM). Due to high experimental cost and number of the operational parameters the development of numerical simulations to predict part quality is growing. Despite this, erroneous assumptions and simplifications limit the application of HP-RTM models, specifically with regards to the energy models used to model the heat transfer occurring during infiltration. The current work investigates the operating parameters at which the thermal non-equilibrium energy model’s increased computational cost and complexity is worth added accuracy. It was found that in nearly all cases, using the thermal non-equilibrium is required to obtain an accurate prediction of the temperature development and resulting final properties within the mold after the infiltration process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".