Aerodynamic Optimization and Experimental Analysis of Shrouded Rotor Blades
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs), particularly Vertical Take-Off and Landing (VTOL) aircraft such as quad-rotors and helicopters, have gained attention for diverse applications in military and civilian domains. However, to increase applications, reducing their power consumption and their restricted payload capacity. This paper describes a method to enhance the thrust capabilities of typical shrouded rotors through a novel rotor design. Beginning with an airfoil with a high lift-to-drag ratio. Blade element momentum theory (BEMT) is used to optimize the rotor's chord and twist distributions systematically along with precise induced velocity prediction in shrouded rotors. Furthermore, a validation process requires rotor manufacturing and experimentation. BEMT harmonizes momentum and blade element theories, offering a comprehensive framework for rotor behavior modeling, especially in hovering conditions. First, second, and third degrees functions are used to express both the chord and twist distributions along the rotor radius from where the best rotor design is obtained, for this, an experimental validation is employed. The experimental tests demonstrate improved performance, especially when the rotor designed using the higher degree functions is employed. The proposed approach provides a comprehensive approach to shrouded rotor design, offering advancements.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".