Analysis Driven Design and Optimization Methods for Aircraft Structures using Finite Element Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it