Flow measurement and thrust estimation of a vibrating ionic polymer metal composite
Bibliographic record
Abstract
Ionic polymer metal composites (IPMCs) are an emerging class of soft active materials that are finding growing application as underwater propulsors for miniature biomimetic swimmers. Understanding the hydrodynamics generated by an IPMC vibrating under water is central to the design of such biomimetic swimmers. In this paper, we propose the use of time-resolved particle image velocimetry to detail the fluid kinematics and kinetics in the vicinity of an IPMC vibrating along its fundamental structural mode. The reconstructed pressure field is ultimately used to estimate the thrust produced by the IPMC. The vibration frequency is systematically varied to elucidate the role of the Reynolds number on the flow physics and the thrust production. Experimental results indicate the formation and shedding of vortical structures from the IPMC tip during its vibration. Vorticity shedding is sustained by the pressure gradients along each side of the IPMC, which are most severe in the vicinity of the tip. The mean thrust is found to robustly increase with the Reynolds number, closely following a power law that has been derived from direct three-dimensional numerical simulations. A reduced order distributed model is proposed to describe IPMC underwater vibration and estimate thrust production, offering insight into the physics of underwater propulsion and aiding in the design of IPMC-based propulsors.
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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 | 0.001 | 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 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".