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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".