Aerodynamic Shape Optimization of an S-Duct Intake for a Boundary-Layer Ingesting Engine
Bibliographic record
Abstract
A high-fidelity aerodynamic shape optimization framework based on the Reynolds-averaged Navier–Stokes equations is applied to the optimization of a boundary-layer ingesting S-duct designed for embedded engines on a high-subsonic, unmanned flight vehicle. The optimizations initially target a cruise operating condition and are further extended to single-point and multipoint optimizations considering descent and climb. Two different composite objective functions are used. The first combines distortion and swirl at the fan interface plane as well as total pressure recovery, with user-defined weights for each objective, whereas the second involves pressure recovery, fan blade load variation, and fan blade incidence variation. Pareto fronts show the tradeoffs between objectives. The results indicate that compared to the baseline geometry, a simultaneous improvement in all objectives contained in the composite objective function can be obtained, depending on the priorities of each objective pre-assigned by the user. It was also found that if swirl can be ignored, then fan-face distortion can be greatly reduced while simultaneously reducing total pressure loss in the S-duct. Similarly, fan blade load variation and fan blade incidence variation can be significantly reduced while reducing total pressure loss. Finally, the multipoint optimization results show that a single S-duct geometry can perform well during cruise, climb, and descent conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".