System-Level Performance of Microturbines With an Inside-Out Ceramic Turbine
Bibliographic record
Abstract
Ceramic turbines can reduce fuel consumption by increasing turbine inlet temperatures (TIT). The need for heat-resistant materials like ceramics is particularly acute for small turbomachines for which efficiencies are limited by the use of uncooled metal turbine as complex cooling schemes are impractical and costly. Efforts to introduce ceramics in the turbine rotor were made between the 1960s and the 1990s by gas turbines and automotive manufacturers in the U.S., Europe, and Japan. While significant progress was made, a suitable level of reliability still cannot be achieved as the brittleness of ceramics leads to crack propagation in the blades loaded in tension and catastrophic failure. The inside-out ceramic turbine (ICT) is a design alternative specific to ceramics that loads the blades in compression by using an outer, air-cooled composite rim that sustains the centrifugal loads. This paper provides an analytical model based on the Brayton cycle to compute the system-level performance of microturbines using an ICT. Loss submodels specific to ICT architectures are developed to account for: (1) composite rim drag, (2) composite rim cooling, (3) leakage through rotating seals, and (4) expansion heat losses. The thermodynamic core model is validated against three state-of-the-art, non-inside-out, microturbines. Based on a Monte Carlo simulation that takes into account the modeling uncertainties, the model predicts a cycle efficiency of 45±1% for a 240 kW ICT-based microturbine, leading to a predicted reduction in fuel consumption of 20% over current all-metal microturbines.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".