CFD optimization of an S-shaped engine diffuser with a boundary layer ingestion configuration
Bibliographic record
Abstract
Future aircraft designs such as the blended wing body have the potential to reduce fuel consumption from 20% to 30% compared with the classical configuration. This also gives the opportunity to integrate the engines into the wing. Burying the engines brings many benefits including noise shielding and a theoretical increase in propulsive efficiency owing to boundary layer ingestion (BLI). However, BLI causes nonuniformity in the flow stream at the inlet, which can reduce the pressure recovery and increase the distortion coefficient at the engine fan face. These factors reduce the overall efficiency and stall margin of the engine, in turn reducing the potential benefits of the design. The primary objective of this research was to identify, with a parametric study, the geometric design variables of the S-shaped diffuser, which has an impact on the nonuniformity of the flow stream in the case of BLI by the engine as well as its role in distortion and pressure recovery variation. The secondary objective is to propose an optimization process aimed at reducing fuel consumption based on a CFD analysis coupled to a thermodynamic module. Results showed that the length of the diffuser and the aspect ratio of its air intake are the two most important geometric variables affecting the installation. They also showed a reduction in fuel consumption of 0.8% between the worst and optimal configurations. However, the study concluded that the optimized diffuser still exhibits an unacceptable level of distortion, which can compromise the durability of the engine components.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".