DOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions
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Bibliographic record
Abstract
The need of reducing the dependence of fossil fuels and CO2 emissions have motivated the diversification of energy matrix. Among the Renewables, the hydropower shows better characteristics compared to solar, wind, biomass and geothermal, because its low CO2 emissions, higher density and others technical factors. Within the Hydropower, the Hydrokinetic turbines (HT) are considered as a promising technology because can provide electricity during low flow velocity conditions (< 2 m/s) and is able to operate in shallow waters < 8 m and in secluded areas without access to the energy network. In this sense, the present study incentivizes the research in Hydropower and proposes and new application of DOE-ANOVA combined with Computational Fluid Dynamics (CFD) modelling for the HT design and optimization. Accordingly, this work evaluated the performance of a HT with 1.9 m of rotor diameter operating in a water flow of 1.5 m/s through a 23 factorial design with 9 modelling cases (MC). The results showed that the increment of outlet diameters increased the downstream velocity and the hydrodynamic pressure over the HT, and the reduction of the blade tip edge distance generated an increment of the response of the HT hydraulic and mechanical properties.
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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 it