Optimal turbine blade design enabled by auxetic honeycomb
Bibliographic record
Abstract
Abstract Gas turbine blades are subjected to unusually harsh operating conditions—rotating at high velocities in gas streams whose temperature can exceed the melting temperature of the blade. In order to survive these conditions, the blade must efficiently transfer heat to an internal cooling flow while effectively managing mechanical stresses. This work describes a new design strategy for the internal structure of turbine blades that makes use of architected materials tailored to reduce stresses and temperatures throughout the blade. A full 3D characterization was first performed to determine the thermomechanical properties of generalized honeycomb materials with different design parameters: honeycomb angle and wall thickness. A turbine blade cross section was then divided into multiple discrete domains so that different generalized honeycomb materials could be assigned to each of the domains. Optimization of the material assignments was performed in order to minimize the stress ratio—ratio of the maximum Mises’ stress and the temperature dependent yield stress—in the entire model. The optimized design showed substantial improvement with respect to a baseline model; the factor of safety was increased by 171%, while the maximum Mises’ stress and temperature decreased by 42% and 72% respectively. The use of generalized honeycomb materials allows for local control of the material properties to tune the performance of the turbine blade. The results of the optimization clearly indicate that auxetic honeycombs outperform conventional designs; since their lower in-plane stiffness helps to reduce stresses caused by thermal gradients. Our results demonstrated the feasibility of using 3D-printing compatible architected materials in turbine blades to increase their factor of safety and potentially increase operating temperatures to improve thermal efficiency.
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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 | 0.000 |
| 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) | 0.001 | 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".