Numerical investigation of nanofluid heat transfer in the wall cooling panels of an electric arc steelmaking furnace
Bibliographic record
Abstract
Abstract Wall cooling panels are typically a kind of electric arc furnace equipment that has precisely influence on different aspects of the steelmaking process. This investigation employs a CFD method to evaluate the thermal performance of water cooling panels in real operating conditions to validate the numerical method followed by replacing cooling water with Al2O3/Water nanofluid coolant. The results are revealed that the high rate of receiving heat flux and generated vortexes with low-velocity cores lead to hot spots inducing on bends and elbows. In the operating flow rate, the maximum temperature of the hot-side wall decrease by 14.4% through increasing the nanoparticle concentration up to 5%, where the difference between maximum temperature and average temperature on the hot-side decrease to 12 degrees. According to the results, use of nanofluid coolant is a promising method to fade the hot spots out on the hot-side and gifting a lower and smoother temperature distribution on the panel walls of thereby prolonging the usage period of panels.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".