Component Model Parameter Updating for Landing Gear Linkages with Flexible Joints
Bibliographic record
Abstract
This paper presents a practical and effective framework for updating landing gear finite element component models such that the updated reduced-order component mode synthesis model of the entire assembled system can provide accurate structural dynamic results in any possible landing gear configuration. By constructing component mode synthesis matrices using updated finite element parameters, the requirement to experimentally derive the modes required for component mode synthesis is avoided. Because it is impossible to directly measure joint frequency response functions, the experimental substructure boundary conditions are made to mimic natural joint-link connectivity by using flexible-hinged boundaries with links and joint hinges in arbitrary orientations. This exploits the fact that experimental frequency response functions obtained away from the joints can contain information about the joint dynamics, which result in resonance frequency shifts. The updating of each component model in multiple possible configurations is performed using a genetic algorithm with bounded inequality constraints on the updating parameters and nonlinear constraints on frequency response function parameters (natural frequencies, antiresonances, and fixture motion). Afterward, the updated flexible-hinged supports are analytically removed for final system component mode synthesis assembly. The effectiveness of the proposed framework is demonstrated with an experimental case study on a simplified landing gear model. Overall, the updating method can enable further dynamic testing of the complete system in a virtual environment, thereby reducing the need for extensive experimental testing on entire landing gear assemblies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".