Parallel Finite Element Approach for large Thermal Problems Applied to Glass Bending Furnace
Bibliographic record
Abstract
In this paper, iterative sub-structuring methods are applied to an FEM discretized model of an industrial glass thermoforming furnace.The FEM-based model is dubbed the component interaction network (CIN) and has been previously used in many thermal problems modelling.Applying iterative sub-structuring methods to CIN led to the creation of multi-CIN, used for large heat problems, in particular the glass thermoforming furnace problem.The substructures are then simulated simultaneously using the same direct thermal solver used for the general problem.Multiprocessing technology was exploited for parallel solving of the sub-structures, while a coupling algorithm handled interfacial coupling.In addition, a time-marching scheme was developed, including a coarse numerical problem solving for stabilization phases and adaptive time stepping for stable repetitive phases.The model was compared to the experimental measurements.The results were met with good reproduction of the thermal behavior of the components and acceptable accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".