Structural Design Optimization for Unmanned Aircraft Propeller Blades Using the Multi-Objective Colonial Competitive Algorithm
Bibliographic record
Abstract
The rapid evolution of unmanned technology demands the development of optimized propeller designs that can accommodate a wide range of flight conditions and mission requirements.This article suggests a multi-objective optimization framework for unmanned aircraft during the cruising phase, based on the Multi-Objective Colonial Competitive Method (MOCM).This paper focuses on the maximum thrust-to-weight ratio at hover (T-WHmax) as one of its objective functions, which is associated with the ability to resist wind and maneuver effectively during takeoff and landing.The total energy consumption is the second objective function.Using the suggested framework for the Airbus Vahana unmanned aircraft, the structure of the propeller blade (PB) is optimized and verified through computational fluid dynamics (CFD).A detailed analysis is conducted on the effects of the hover disk loading and cruising speed on the optimization outcome.The findings indicate that T-WHmax greatly influences the outcome of optimization.A comparison with literature results proves the benefits of the optimal PB design in both saving energy and improving takeoff maneuverability.In general, the method and guidelines outlined in this paper endorse the structural optimization of PB design for unmanned aircraft.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".