Prediction of heat transfer coefficient during quenching of large size forged blocks using modeling and experimental validation
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Bibliographic record
Abstract
In this study, a new method is developed to predict an accurate convective heat transfer coefficient (HTC) during quenching of large size steel blocks, using a combination of 3D Finite Element (FEM) simulations and a progressive artificial neural network (ANN). The HTC profile of the first inputs used for FEM simulations were acquired from the literature to calculate the cooling temperature profiles at specific locations. The training of the ANN was set up between HTCs and their corresponding FEM-calculated temperature. Experimental validation was carried out by instrumenting a large size forged steel block during the quench process. The experimental cooling curves were used for validation of the FEM simulation, as well as for the prediction of new HTCs by simulating the ANN. Results show that the proposed method provides progressively more accurate predictions than the existing ones reported in the literature. A mean absolute percentage error (MAPE) of 1.47% was found between experimental and calculated cooling curves for the predicted HTC, further demonstrating a better prediction ability of the proposed method.
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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 it