Geometric Optimization of Aggressive Inter-Turbine Ducts
Bibliographic record
Abstract
To reduce the harmful effects of aviation on the environment, aircraft gas turbine manufacturers continue to focus on producing engines with lower specific fuel consumption and weight. To address the engine weight challenges, R&D efforts continue to center around extending aerodynamic design limits, thus enabling reduced airfoil/stage count, reducing engine length or some combination thereof. The inter-turbine transition duct (ITD), located between the high-pressure (HP) and low-pressure (LP) turbines, is one of the components for which potentially significant weight reduction can be achieved through aggressive aerodynamic designs. Such ducts could have larger HP-to-LP radial offset and/or shorter length resulting in Aggressive Inter-Turbine Ducts (AITD). This paper presents a geometry optimization process to design AITD with minimum total pressure losses. Geometry optimizations were performed using the built-in optimization process in NUMECA Fine/Turbo 8.7. To evaluate the optimization process, one baseline ITD geometry was first generated with the same inlet and outlet coordinates as an existing ITD. The performance of the optimized ITD was studied numerically in comparison with the existing ITD. After the evaluation study, a second ITD geometry with more aggressive parameters, equivalent to increasing mean rising angle by 25% was optimized. Based on the studies of those two optimized geometries, a generic design rule of ITD with mild parameters was developed and the third ITD geometry with increased 20% area ratio (AR) was designed. The performance of designed ITDs was investigated numerically and the results are discussed in the paper.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".