River water surface velocity measurement using large-scale particle image velocimetry
Bibliographic record
Abstract
It is proposed to use advancements in Large Scale Particle Image Velocimetry (LSPIV), such as improved charge-coupled device in cameras, unmanned aerial vehicles, and faster algorithms, for a non-invasive river water surface velocity measurement to assess potential hydrokinetic turbine sites. The approach will compare results to an Acoustic Doppler Velocimeter (ADV). Being able to measure the water surface velocity using the proposed method allows the use of a low-cost and simple approach to determine hydrokinetic sites suitable for turbine deployment for electrification of remote communities. The research tests were conducted in a water tunnel and at the Canadian Hydrokinetic Turbine Testing Center located in Winnipeg River using cameras and a drone with results compared to an ADV. Two LSPIV simulation software’s PIVlab and OpenPIV were used to analyze the water surface velocity captured in a water tunnel and at the Canadian Hydrokinetic Turbine Testing Center. The results of the LSPIV software analysis with optimized average velocity data results from PIVlab are within ±0.1 m/s from the average ADV velocity results. In addition, optimized velocity data from PIVlab show vector results moved closer to the ADV measured water surface velocity and resulted with fewer fluctuations after the erroneous data was removed. Thus, a non-invasive way of analyzing water surface velocity by flying a drone overhead capturing the water surface and using LSPIV software such as PIVlab and OpenPIV to extract data from the captured images could replace conventional invasive methods of water surface velocity measurements for site assessments so long as these are done in good weather, minimal wind and water surface ripples are clear, detailed and defined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".