Comprehensive Soft Impact Damage Methodology for Advanced High Bypass Ratio Turbofan Engines
Bibliographic record
Abstract
Foreign object strikes are one of the major aviation incidents that cause tremendous risk to both the aircraft and passengers, the adverse implications of which can ripple through the aviation industry. The demand for air transportation has been on the rise, leading to foreign object ingestion into the engines becoming one of the most threatening scenarios. In January 2009, Flight 1549 took off from LaGuardia Airport in New York City and struck a flock of Canadian Geese during takeoff. Both engines ingested birds, resulting in mid-air catastrophic engine failure. Fortunately, none of the damaged engine components penetrated into the cabin and the aircraft successfully completed an emergency landing on the Hudson River without incurring any casualties. In this work, explicit finite element strategies have been adopted to model the Fluid-Solid Interactions (FSI) present in a bird ingestion scenario. Taking into account the fluidic composition of bird bodies, a proper methodology to model the Fluid-Solid Interaction was implemented. The investigations were aimed to understand the significance of impact force histograms in an accurately represented model, analyze shockwave propagations and reflections during the impact window, and perform parametric studies to highlight a superior representation for the Fluid-Solid Interaction (FSI). The paper presents a modern explicit finite element methodology adopted to accurately model bird ingestion into a complex turbomachinery forward section and analyze subsequent failure sequencing of the relevant structural components within the system.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".