Estimating the Drag Developed by a High Bypass Ratio Turbofan Engine
Bibliographic record
Abstract
A high bypass ratio turbofan engine capable of powering the Boeing 757 was considered for thrust and drag analysis. A quasi-2D engine model applying the fundamental thermodynamics conservation equations and practical constraints determined engine performance and provided cross-sectional areas in the low-pressure system. Coupled with suggestions on boat-tail angle and curvature from literature, a representative bypass duct and primary exhaust nozzle was created. 3D steady-RANS simulations using Fluent® 18 were performed on a 1/8th axisymmetric section of the geometry. A modified 3D fan zone model forcing radial equilibrium was used to model the fan and bypass stator. Takeoff speed and cruise operating conditions were modeled and simulated to identify changes in thrust composition and intake sensitivity. Comparison between net thrust predictions by the engine model and measured in CFD were within grid uncertainty and model sensitivity at cruise. Trends observed in a published database were satisfied and calculations coincided with GasTurb™ 8.0. Verification of thrust in this manner gave confidence to the aerodynamic performance prediction of this modest CFD. Obtaining a baseline bypass design would allow rapid testing of aftermarket components and integration techniques in a realistic flow-field without reliance on proprietary engine data.
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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 | 0.001 | 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".