A Numerical Thermal Analysis of the Heating Process of Large Size Forged Ingots
Bibliographic record
Abstract
Simulation and analysis of thermal interactions during heat treatment is of great importance for accurate prediction of temperature evolution of work pieces and consequently controlling the final microstructure and mechanical properties of products. In the present study, a three-dimensional CFD model was employed to predict the heating process of large size forged ingots inside an industrial gas-fired heat treatment furnace. One-ninth section of a loaded furnace, including details such as fixing bars and high-momentum cup burners, was employed as the computational domain. The simulations were conducted using the ANSYS-FLUENT commercial CFD package. The k-ε, P-1 and Probability Density Function (PDF) in the non-premix combustion, as low computational cost numerical approaches were employed to simulate the turbulent fluid flow, thermal radiation, combustion and conjugate heat transfer inside the furnace. Temperature measurement at different locations of the forged ingot surfaces were used to validate the transient numerical simulations. Good agreement was obtained between the predictions of the CFD model and the experimental measurements, demonstrating the reliability of the proposed approach and application of the model for process optimization purposes. Detailed analysis of conjugate heat transfer together with the turbulent combustion showed that the temperature evolution of the product was significantly dependant on the furnace geometry and the severity of turbulent flow structures in the furnace.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".