Multi-Objective Shape Optimization of a Boundary Layer Ingesting Engine Intake
Bibliographic record
Abstract
The growth of the airline industry has highlighted the need for more environmentally conscious aviation leading to the conceptualization of more fuel efficient aircraft.One concept that has received significant attention and has been associated with improved fuel efficiency is the Boundary Layer Ingesting (BLI) propulsion system, which refers to the ingesting of the aircraft wake by the propulsors.Although BLI has theoretically been proven to reduce fuel burn, this can potentially be offset by the reduced efficiency and stability experienced by the propulsor in the presence of distorted inflow.Therefore, engine intakes must be optimized in order to mitigate the effects of BLI on the propulsion system.In this work, the shape optimization of a BLI intake is investigated using a free form deformation technique in combination with a multi-objective genetic algorithm, in order to minimize pressure losses and distortion at the engine inlet.The optimization is performed on both a "straight-through" duct and a S-duct intake at a cruise altitude of approximately 37,000 feet and a free stream Mach number of 0.8 and 0.7, respectively.An optimization strategy was developed for the task which was able to produce a Pareto optimal set of designs with improved pressure recovery and distortion for both intakes.The general trend of the optimal designs show that to reduce distortion the optimizer accelerates the flow to reduce the size of the low total pressure region and increase the dynamic pressure at the engine inlet.In contrast, the pressure recovery was increased by reducing velocity as well as shifting the maximum velocity region to the outlet, which reduces the viscous dissipation losses within the intake.y + Non-dimensional wall distance -Greek Symbols α DC(60) sector angle δ Boundary layer thickness Φ Dissipation xiii
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".