Power-Density vs Efficiency Trade-Off for a Recuperated Inside-Out Ceramic Turbine (ICT)
Bibliographic record
Abstract
Abstract Recuperated cycles can significantly increase the efficiency of small gas turbines that are today operating with low pressure ratios and uncooled or lightly cooled turbine blades. However, for mass-driven applications such as aeroengines, the efficiency benefit is typically outweighed by the increased weight associated with the heat exchanger (HX). Increase in specific power could overcome this penalty by reducing the mass flow through the system and therefore its weight and size. To do so, the Turbine Inlet Temperature (TIT) must be increased by ∼250 K over state-of-the-art small gas turbines. The Inside-out Ceramic Turbine (ICT) propose a new path to increase TIT of small turbines, where blade cooling schemes are impractical and costly. This new architecture increases the achievable TIT by using ceramic blades loaded in compression under centrifugal loads supported by an air-cooled rotating composite rim. This paper provides a system-level evaluation of the power-density to efficiency trade-off for the sub-megawatt class turbines using the ICT configuration. The numerical simulation includes 3 submodels to provide cycle efficiency and mass estimates for various cycle and HX design: (1) a station-based thermodynamic model; (2) a 1D-FEM HX model for a straight counterflow recuperator; and (3) a system-level mass model of the recuperated engine configured for a turboprop or turboshaft. At a TIT of 1550 K, the optimal ICT configuration provides a power density of 3 kW/kg and 40% thermal efficiency, which is 4 times lighter than recuperated turbines at 1300 K for the same efficiency level. Further increase in TIT to 1800 K would reach current state-of-the-art turboprop power densities (up to 5 kW/kg) while still achieving over 40% thermal efficiency or — for applications where power density can be traded for efficiency — up to 50% thermal efficiency while maintaining low pressure ratios and associated simplicity.
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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".