Impact of Triply Periodic Minimal Surface Structure Unit Cell Size on Transpiration Cooling
Bibliographic record
Abstract
Transpiration cooling is a highly promising technology for use in thermal management in modern gas-turbine-based aeroengines, providing superior cooling effectiveness compared to state-of-the-art effusion cooling methods. However, conventional porous media such as metallic foams and sintered metals, when used for transpiration cooling, face challenges in achieving desired engineering properties, such as surface texture, mechanical strength, and porosity profile. To achieve the full potential of transpiration cooling by overcoming the challenges inherent in conventional porous media, a precisely engineered deterministic porous medium is desirable. Recent advancements in additive manufacturing have enabled the fabrication of porous media with highly precise lattice structures. Among candidate lattice structures, triply periodic minimal surface (TPMS) structures are considered ideal due to their superior mechanical strengths and fully interconnected, periodic internal channels and external texture patterns to facilitate cooling film development. This study aims to investigate the impact of unit cell size of TPMS lattices on adiabatic film cooling effectiveness using binary pressure-sensitive paint (PSP). Diamond-type TPMS lattices of a fixed actual porosity (36%) but of three distinct unit cell sizes (1.9, 2.5, and 3.1 mm) were examined at three injection ratios ([Formula: see text], 1.14%, and 1.60%). Results show that smaller unit cell sizes lead to more effective and more uniform cooling films. Enhanced transpiration cooling effectiveness at smaller unit sizes might be attributed to shallower surface voids and reduced lateral flow within the porous medium. The findings are expected to guide the design of TPMS-based transpiration cooling systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".