Fluid-Structure Interaction Analysis on Cantilever Beams for Micro-Energy Harvesting of Cross-flow Turbine
Bibliographic record
Abstract
The harvested electrical energy is used in a variety of electronic equipment such as remote sensors, automobiles, medical or military equipment, etc.As the energy demand increases, the need for an efficient energy harvesting system increases and the relevant researches are actively carried out [1][2][3][4][5].Cross-flow turbine is a water impulse turbine with relatively low efficiency, but it can be adjusted at various flow rates and is easy to maintain.As the working fluid passes through the impeller of the cross-flow hydraulic turbine and forms a vortex field at downstream, the induced vortex flow can be used for converting the kinetic energy inherent in vibrations to electricity using energy harvesters such as cantilevers, membranes or other structures.In this study, the cantilever beams were located at the downstream of cross-flow hydraulic turbine for microenergy harvesting.Numerical analysis was conducted using the commercial code, ANSYS CFX 18.1 with the k-ω based shear stress transport (SST) turbulence model.The effect of distance between cantilever beams on stress and strain was evaluated using 2-way fluid-structure interaction (FSI) analysis.As a result, the maximum von-Mises stress of the cantilever beam was calculated as 163.5MPa, and the maximum deformation was calculated as 2.29mm.In addition, the results were graphically depicted with various geometrical and flow conditions.
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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".