Hydrokinetic Vortex Induced Vibration (VIV) Energy Harvester
Bibliographic record
Abstract
Abstract This paper investigates the performance of vortex-induced vibration (VIV) energy harvester in low-speed water flow. The proposed VIV harvester is extracting hydrokinetic energy from the flowing current and transferring it into mechanical vibrations. The vibrations are further converted into electrical energy using the piezoelectric transducer to supply the modern demand for energy-consumption. To meet the demand, the single harvester is analyzed to determine the suitable geometry for the bluff body that is sensitive to the low-speed flow. Furthermore, the converter must be able to harvest vibrations of varying amplitudes and frequencies. To maximize the power output, different array configurations of multiple bluff bodies are examined. A single positively buoyant elastically mounted cylinder is tested experimentally and at a low flow speed of 0.3 m/s, it can harvest vibrations with an average frequency of 1.8 Hz and peak to peak amplitude of 1.5d, where d is the diameter of the bluff body. It was found that for an array consisting of ten bluff bodies, the average frequency and peak to peak amplitude increases to 2.09Hz and 1.54d, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".