Influence of Spacers and Skid Sizes on Heat Treatment of Large Forgings within an Industrial Electric Furnace
Bibliographic record
Abstract
The influence of stacking patterns, through the different spacer and skid sizes, on the transient temperature distribution uniformity of large-size forgings in a 112-m3 electrical heat treatment furnace was investigated by conducting CFD simulations and real-scale experimental validation. A 3D CFD model of the electrical furnace was generated, including a heat-treating chamber, axial flow fans, large size blocks, skids, and spacers. Real-scale temperature measurements on instrumented test blocks during the heat treatment process were carried out to validate the CFD simulations. Results indicated that the CFD model was capable enough to determine the transient temperature evolution of the blocks with a maximum average deviation of about 6.62% compared to the experimental measurements. It was found that significant temperature non-uniformities of up to 379 K on the surfaces of the blocks due to the non-optimum stacking pattern were experienced by the blocks. Such non-uniformities could be reduced between 24% to 32% if well-adapted spacer and skid sizes were used in the stacking configurations. Based on simulation results and experimental validation work, optimum spacer and skid sizes for uniform temperature distribution were proposed for different stacking patterns.
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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 | 0.001 |
| 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 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".