Using Numerical Analysis to Design and Optimize River Hydrokinetic Turbines’ Capacity Factor to Address Seasonal Velocity Variations
Bibliographic record
Abstract
Seasonal velocity variations can significantly impact the total energy delivered to microgrids produced by river hydrokinetic turbines. These turbines typically use a diffuser to increase the velocity at the rotor section, adding weight and raising deployment costs. There is a need for practical solutions to improve the capacity factor of such turbines. Our solution involves using multiple turbine rotors that can be interchanged to match seasonal velocity changes, eliminating shrouds to simplify design and reduce costs. This solution requires turbines that are designed to have an easily interchanged rotor, which requires us to limit the rotor to a two-blade design to also lower costs. This approach adjusts the turbine power curve with different two-blade rotor sizes, enhancing the yearly capacity factor. BladeGen ANSYS Workbench is used to design three two-blade rotors for free stream velocities of 1.6, 2.2, and 2.8 m/s. For each turbine rotor, 3D simulation is applied to reduce aerodynamic losses and target a coefficient of performance of about 45%. Mechanical stress analyses assess the displacement and stress of the used composite materials. Numerical results show good agreement with experimental data, with rotor efficiencies ranging from 43% to 45% at a tip speed ratio of 4 and power output between 5.4 and 5.6 kW. Results show that rotor interchangeability significantly enhances the turbine capacity factor, increasing it from 52% to 92% by adapting to river seasonal velocity changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".