Biomimetic Dolphin-Inspired Fin Designs and Underwater Prosthetic Applications
Bibliographic record
Abstract
This study synthesizes contemporary research on dolphin-based fins and applies the acquired concepts to the design of prosthetic limbs for amputee swimmers. Three specific studies are assessed, namely: 1) The fluid–structure interaction of bionic dolphin tail fins reveals that flexible fins with greater dynamic thickness at the leading edge and varying thickness profiles offer enhanced propulsion and efficiency at biologically relevant Strouhal numbers, when compared to uniform rigid non-biomechanical fins; 2) an adaptive fin featuring a shear-stiffening gel “compound joint” fin, which dynamically alters between relatively soft and stiff states, generates propulsion comparable to that of rigid fins while exhibiting significantly less fluctuation in the applied force; and 3) a fin emulating the stiffness gradient of a sunfish provides approximately 25 - 26% greater propulsion for a constant input power compared to a uniform stiffness control, indicating that stiffness gradients are advantageous over constant stiffness. Collectively, these studies illustrate the benefits of biomimetic fins, including tailored flexural properties, optimized geometries, variable material properties, and organized vortex shedding, which contribute to an increase in propulsion, efficiency, and stability. The latter part of the report applies these concepts to real-world instances, such as The Fin, AMP Fins, and prosthetic animals like Winter's dolphin tail, demonstrating how biomimetic fins not only enhance speed and maneuverability but also improve comfort and satisfaction for gene-reduced systems in comparison to conventional paddle-type devices. Overall, the available evidence suggests that biomimetic fin architecture represents a feasible approach for the design of next-generation underwater prosthetics.
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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 | 0.000 |
| 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) | 0.001 | 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".