Design Optimization of Multifunctional Aerospace Structures
Bibliographic record
Abstract
Multifunctional structures are crucial for achieving least-weight and performance missions in designing contemporary complex systems. Especially in the aerospace industry, integration of several subsystems into an existing structural assembly can lead to significant mass and volume reduction, which subsequently improves measures such as range, speed, and fuel efficiency of air transportation vehicles. It is important then to underline the advantages of multifunctional structures and showcase their successful applications. Computational design and optimization tools are vital for the development of multifunctional structures, enabling the delivery of the intended functionalities while remaining competitive with conventional designs. Topology optimization in particular has grown tremendously to become a standard tool in early design stages due to offering indispensable insights about the load paths forming in the structure. In addition to an overview of multifunctional structures and computational design optimization techniques applicable, this chapter highlights practical cases by presenting the design of a novel shimmy damper mechanism for aircraft nose landing gears. The concept of this shimmy damper is briefly explained, and the design optimization framework is described along with the outcome of that procedure. The novel design enables integration of the vibration dampening mechanism into the torque links system, resulting in a multifunctional system retrofittable to existing nose landing gears while avoiding any asymmetry in the load and mass distribution. The re-imagination of the torque link system made possible through a multifunctional design philosophy highlights the relevance and significance of multifunctional structures in current and future aerospace systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".