Bibliographic record
Abstract
A Python–FreeCAD automated program was developed to optimize spar cross‐sections for tandem‐wing UAV applications under set deflection limits. Initial I‐beams and C‐channels were generated based on spanwise lift distributions from XFLR5 and analyzed using a simplified 2D beam bending model. Cross‐sectional inertias were calculated via the parallel‐axis theorem, and masses were obtained directly from FreeCAD volume queries. A non‐destructive, monotonic “geometric sweep” optimizer reduced each section’s inertia by 5 % per iteration until the calculated deflection precisely met the target span‐to‐deflection ratio of 12/67% to five‐decimal‐place accuracy. The final flange and web dimensions were then reconstructed in CAD and extruded to the appropriate lengths, with STEP exports produced for both the baseline and optimized beams. Results showed significant mass savings while maintaining structural stiffness, the optimized beams achieved target deflections without overshoot, and the original designs we preserved for side‐by‐side comparison.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".