Effects of Lattice Orientation Angle on TPMS-Based Transpiration Cooling
Bibliographic record
Abstract
Abstract Establishing a continuous cooling film is an effective way to thermally protect hot-gas path components of gas turbines. For aero-engines, effusion cooling is the state-of-the-art method for developing cooling films. However, the cooling film generated by this method is far from ideal, as discrete miniature cooling air jets exiting from effusion cooling holes leave large gaps between cooling holes without adequate cooling film protection. Furthermore, effusion cooling jets can experience strong lift-off from component surfaces and are subsequently diluted due to mixing with the main flow. Recent advances in additive manufacturing (AM) technologies have enabled the fabrication of porous materials with precisely engineered lattice structures, significantly enhancing the film cooling effectiveness of hot-gas path components through transpiration cooling. A prior study has demonstrated highly promising transpiration cooling results by using a family of lattice geometries referred to as triply periodic minimal surface (TPMS) lattices. The present study experimentally investigates the influence of various TPMS lattice orientations on the film cooling effectiveness. Three types of TPMS structures, namely Diamond, Koch and Gyroid, are compared to demonstrate that the TPMS lattice orientation angle affects transpiration cooling performance, with different levels of sensitivity according to the TPMS structure. The TPMS lattice structures studied in this investigation are fabricated by stereolithography (SLA) 3D printing. The adiabatic cooling film effectiveness (AFE) is measured using pressure sensitive paint (PSP).
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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) | 0.000 | 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) | 0.002 | 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".