Using numerical analysis to design and optimize river hydrokinetic turbine to address seasonal velocity variations
Bibliographic record
Abstract
Seasonal velocity variations can significantly impact 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 increasing deployment and retrieval costs. There is a need for practical solutions to improve the capacity factor of such turbines for remote communities. Part of the solution is addressed by developing multiple turbine rotors that can be interchanged to match seasonal velocity variations and thereby eliminate the need for shrouds to simplify the design and reduce costs. The proposed approach employs different rotor sizes to address seasonal river velocity changes, modifying the turbine power curve to increase the yearly river turbine capacity factor. A turbine design that can be surfaced using boats available in remote communities is used to allow 2 blades rotor changes. BladeGen ANSYS Workbench is used to design the three rotors of decreasing size for free stream velocities of 1.6, 2.2, and 2.8 m/s. For each hydrokinetic turbine rotor, the 3D simulation is applied to reduce aerodynamic losses and target a coefficient of performance of up to 45%, by optimizing the blade shape and rotor aerodynamic parameters. Mechanical stress analyses determine the maximum displacement and blade stress for stainless steel and composite materials. Numerical results were compared to experimental results: the pressure coefficient against the tip speed ratio demonstrated good agreement with the experimental data. Based on the simulations, the three rotor efficiencies varied from 43% to 45% at a TSR of 4, the point at which the maximum pressure coefficient was observed in numerical and experimental results, while the power output varied from 5.4 to 5.6 kW for the three velocities investigated. Results show that it is possible to significantly increase turbine capacity factors by interchanging rotors to account for seasonal velocity variations in rivers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".