Bibliographic record
Abstract
This report details an investigation which uses a methodical approach for identifying the best design for a morphable rib. This morphable rib is to be integrated with a Flexible Trailing Edge (FTE) design. Following the procedure of this report, 6 initial structure types were considered, specifically, these structures are auxetic. Based on Finite Element Analysis (FEA) simulation results, the best structures were iterated upon. These simulations aimed to find designs which had the greatest reaction force, indicating strength, and a minimal side deformation, which may indicate failure. FEA continued with changes made to specific dimensions, to understand the effects of variations on the original design. Full FTE assemblies were created as well, and loads were applied to the skin and displacements were added to simulate the morphing action. Based on the overall performance of the structure types and assessments of model 3D printed parts, the final ribs for this report were chosen to be the Structure type A, with a ligament thickness of 0.055 in. The ribs have been printed and are pending further testing, to validate simulation results. Future design work would include further iterations as well as integration with the morphing features of the FTE.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".