Numerical Analysis of a Soft Propeller with Flagellum-Inspired Geometry for Underwater Propulsion
Bibliographic record
Abstract
In recent years, soft robots and propellers have emerged as flexible and adaptive alternatives to traditional rigid rotors, offering enhanced morphological versatility and embodied intelligence.However, optimizing their energy efficiency remains a significant challenge.To address this, Reynolds-Averaged Navier-Stokes (RANS) simulations were conducted to investigate fluidstructure interactions around flagellum-inspired geometries.The sliding mesh approach was employed, capturing both transient and mean hydrodynamic loads, such as thrust and torque for two rotating configurations operating at 90 and 60 RPM.This enabled a detailed comparison of their propulsion performance.The 90 RPM propeller, with a diameter of 0.37 m, demonstrated superior performance by generating 0.60 N of thrust, an axial induced velocity of 0.0528 m/s, and a peak flow velocity of 1.72 m/s.However, the 60 RPM configuration, with a slightly larger diameter of 0.40 m, produced a lower thrust of 0.33 N, an induced velocity of 0.0362 m/s, and a peak velocity of 1.26 m/s.Moreover, despite 90 RPM having smaller disk area, achieved a higher local relative velocity of 1.75 m/s and more concentrated flow structures, indicating more efficient energy transfer.These findings highlight the decisive role of rotational speed and geometry in optimizing the performance of soft propellers, with the 90 RPM configuration proving more effective for high-thrust, performance-oriented underwater applications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".