Design of a Human-Powered Aircraft Applying Multidisciplinary Optimization Method
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".