Study of the Effect of Initial Plate Temperature in Jet Impingement Cooling Process
Bibliographic record
Abstract
The microstructure characteristics and the properties of rolled steels are significantly affected by the heat transfer and boiling phenomena occurring during the jet impingement cooling on run‐out tables (ROT). In this study, experiments are conducted using a full industrial‐scale ROT facility with rectangular plates made of low‐carbon stainless steel (type 316L). The plate is heated up to a temperature ranging from to , then rapidly impinged using a single circular water jet, and the temperature drop is captured using an infrared thermal camera (FLIR A615 25°–50 Hz type). The dissipated heat flux, estimated experimentally using a 2D inverse heat conduction analysis, ranges from 6.1 to 3.4 MW m−2 across different zones along the plate surface. The impact of different initial plate temperature on the boiling behavior is studied by developing a 2D‐computational fluid dynamics (CFD) model, and the results are closely aligned with the experimental findings. The results reveal that when estimating the heat flux from CFD simulations, the best accuracy is obtained when considering fluid temperature at a point close to the plate surface (about 1 μm above the surface). Furthermore, the maximum extracted heat flux (MHF) is significantly influenced by the initial temperature of the plate. Increasing the initial plate temperature from 500 to 900 °C led to an increase of 82% in the MHF in stagnation zone, and 137% increase in the parallel‐flow region. The CFD model presented in this study and the full calculation of the boiling curves numerically will pave the road for investigating various practical parameters in jet impingement cooling.
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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 | 0.000 | 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".