Numerical and Experimental Studies of Transpiration Cooling Film Effectiveness over Porous Materials
Bibliographic record
Abstract
Comprehensive experimental and numerical studies were performed to determine cooling film effectiveness (CFE) of transpiration cooling over porous materials. The CFE was evaluated experimentally using pressure sensitive paint (PSP) by invoking heat/mass transfer analogy over the surface of the porous samples. It was found that transpiration cooling can reduce total surface heat flux by two to three times and provide solid surface CFE on average 15% to 30% higher than multi-hole effusion cooling. The numerical model allowed detailed investigation of the flow evolution in the porous media and its ability to create a uniform thermal protection film. Modeling results revealed the flow inside the porous media moves slightly laterally, in the same direction as the main flow due to the viscous effect of the channel flow and low flow resistance provided by the porous samples. This effect causes a large amount of coolant to exit at the trailing edge of the porous media, creating a nonuniform cooling film protection. The model also demonstrates that CFE is dependent on the physical properties (permeability and inertial coefficient) of the porous media. The study indicates that increasing the coolant flow rate increases film protection and that the pore size in the range of 10 to 40 pores per inch does not have a significant effect on the film protection.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".