DOE-ANOVA to Optimize Hydrokinetic Turbines for Low Velocity Conditions
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".