Analysis Driven Design and Optimization Methods for Aircraft Structures using Finite Element Analysis
Bibliographic record
Abstract
The aircraft industry in the present day demands that manufacturers reduce design cycle times and cost whilst still improving product performance in terms of weight, operating costs and environmental impact. One potential method of achieving this is by enabling designers to consider a greater number of design concepts and to allow for more in-depth design studies than are currently possible within the existing design timeframe. This could allow for more informed preliminary design decisions to be made, and ultimately lead to a more optimum product configuration. A method is presented which can rapidly extrapolate preliminary design data to a detailed level for implementation within the initial design phases. The approach enables the linking of global and local analysis and optimization tools in a hierarchical framework which allows greater preliminary design knowledge to be generated and facilitates trade studies so that a range of alternative concepts can be explored. Previous work presented at SDM in 2006 1 demonstrated the approach using conventional stress office analysis techniques. In this paper, the method is extended to incorporate high-fidelity Finite Element Analysis techniques and the two methods are compared to determine the suitability of this approach at the preliminary design stage.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".