New Methodology for Aerodynamic Forces Calculations in Unmanned Aerial Systems Optimization Design
Bibliographic record
Abstract
This study presents AeroStreamPy, a Python-based aerodynamic modeling tool developed to meet the specific design needs of Unmanned Aerial Vehicles (UAVs). Traditional aerodynamic models like Digital Datcom and FDerivatives, while valuable, lack the flexibility and accuracy required for UAV-specific components such as tail booms and complex wing geometries. Physics-based models, including Computational Fluid Dynamics (CFD), provide high fidelity but are resource-intensive, limiting their practicality for rapid UAV design and optimization. AeroStreamPy bridges this gap by combining empirical methods with physics-based potential flow techniques, offering a mid-fidelity model suited for UAV applications. Key features include OpenVSPAero and Athena Vortex Lattice (AVL) for efficient modeling of wings, fuselages, and nacelles under incompressible, inviscid flow, with adjustments for turbulence drag and compressibility effects up to Mach divergence. Validation studies demonstrate AeroStreamPy’s accuracy in calculating aerodynamic coefficients, with ongoing efforts to refine induced drag and interference modeling. Future work aims to integrate AeroStreamPy into optimization processes, and train a neural network-based surrogate model to significantly reduce computational time, enhancing UAV design and performance evaluation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".