Determination of Temperature Distribution during Heat Treatment of Forgings: Simulation and Experiment
Bibliographic record
Abstract
Combination of transient computational fluid dynamics simulations of a gas-fired heat treatment furnace and experimental validation were carried out to investigate the applicability of equilibrium non-premix combustion model and the effect of different turbulence models on the thermal interactions inside the furnace. Thermal interactions analyses based on temperature measurements on an instrumented large size block were performed at different locations of the forged blocks. A good agreement, with a maximum deviation of about 4%, was obtained using a one-third periodic model of the furnace. Results indicated that the chemical equilibrium non-premix combustion model could effectively be employed for combustion modeling and subsequently products’ temperature predictions. A temperature non-uniformity of up to 331 K was determined on the surface of the forgings due to furnace geometrical design and loading pattern. Prediction of turbulence dissipation rate to turbulence kinetic energy ratio by different turbulence models could significantly affect the combustion predictions and product temperatures. Reynolds stress model was found as the most reliable turbulence model and the realizable k-epsilon model could reasonably predict the global block temperature. While, Shear stress transport k-omega model over-predicted the block temperature, it showed reasonable results in stagnation region.
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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) | 0.001 | 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 | 0.001 | 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".