Design of a Human-Powered Aircraft Applying Multidisciplinary Optimization Method
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">A particular field of aerospace engineering is dedicated to the study of aircraft that are so energetically efficient, that the power produced by a human being enables it to takeoff and maintain sustained flight without any external or stored energy. These aircraft are known as Human-Powered Aircraft (HPA). The objective of the present work is to design a single-seat HPA applying multidisciplinary optimization techniques with an objective function that minimizes both the power required and the stall speed, representing respectively, an easier and safer aircraft to fly. In the first stage, a parametric synthesis model is created to generate random aircraft and assess their aerodynamic(utilizing a 3D vortex lattice method code and a component drag buildup method for the drag polar), stability and control(utilizing static stability criteria), weight (estimated using historical data) and performance (using the thus calculated data) characteristics. The aircraft that pass through a set of specified constraints filters form a critical population which is then optimized with a genetic algorithm. The optimal aircraft is chosen from the Pareto frontier and is piloted in a 6-degrees-of-freedom real-time flight simulator coded in Matlab/Simulink.</div></div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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