Desirability function based optimization of experimental data for air-water spray impingement cooling
Bibliographic record
Abstract
The current research copes with the optimization of the surface heat transfer coefficients of a square mild steel test specimen by spray impingement cooling. A laboratory scale experimental setup was developed at School of Mechanical Engineering KIIT University, Odisha, India to investigate the role of various process parameters to enhance the heat transfer from the surface of the heated steal specimen. The mild steel plates of dimension 120 mm 120 mm, and different thicknesses of 4 mm, 6 mm and 8 mm were used in the experiment. The effect of the process parameters such as thickness of the tested plate, nozzle to plate distance, air and water pressure upon the surface heat transfer coefficient (HTC) was optimized. The optimization of the controlling parameters was carried out by using the desirability functions. The Design Expert 8 software was used to analyze the experimental results. A new correlation was developed for optimization of the surface heat transfer coefficient.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".