Gradient Based Simultaneous Structural and Kinematic Optimization of Landing Gear Members based on the Modified Input-Output Equation for Multibody Kinematics
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1672.vid Retractable landing gears are complex multidisciplinary systems. Under the demands of continuously accelerating product development timelines, computationally efficient design tools to finalize lightweight landing gear designs are in high demand. Simultaneous structural and kinematic optimization methodology has been developed in the past to design lightweight retractable landing gears with single-loop planar retraction mechanisms. The objective of this work is to extend previous capabilities with the addition of lock-link mechanisms, actuator performance considerations, enhanced bay constraints and out-of-plane load cases. In this work, retractable nose landing gear designs are generated using the proposed optimization methodology for 3 different commercial aircraft applications. In all cases, the optimizations converged successfully considering the complex mechanisms, new constraints, and loading scenarios. The results demonstrated that multidisciplinary design optimization methodology can accelerate the preliminary design phase of retractable nose landing gears with locking links, out-of-plane load cases and actuator performance also considered.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".