Development of an Inside-Out Ceramic Turbine
Bibliographic record
Abstract
Ceramic turbines could substantially increase operating temperatures of turbomachinery without the need of blade cooling, leading to higher conversion efficiency and power density. However, the inherent brittleness and low tensile strength of ceramic materials limits the use of hub-based ceramic turbines. This paper presents a novel inside-out turbine architecture, permitting the use of monolithic ceramic blades. The proposed architecture uses a carbon-polymer composite rim which converts the rotor radial loads to tangential hoop stress. The blades mainly support compressive loads, minimizing tensile stresses within the blade and thus crack propagation. This allows the use of low tensile strength ceramics which cannot be used in standard hub-based turbines. The rotor hub is comprised of two radially flexible C-shaped hubs, which have sufficient compliance to follow radial displacement of the heavily loaded composite rim. The feasibility of the proposed inside-out ceramic turbine is demonstrated by addressing the four key challenges of the architecture using proof-of-concept prototypes, namely: (1) rotor dynamics of the flexible hub; (2) thermal viability of the composite rim (3) local tensile stress in the blades, and (4) thermal shock in the ceramic blades in transient mode. Experimental validation with an alumina blade confirms that this architecture supports the use of low tensile strength, brittle ceramic blades.
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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 | 0.001 |
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".