Low Wind Speed Characteristics of an Optimized Diffuser Augmented Wind Turbine
Bibliographic record
Abstract
Abstract Towards a final aim of enhancing the feasibility of low-speed sites for wind energy generation, the current study introduces the diffuser augmentation as a method of enhancing the performance of wind turbines designed for poor wind conditions. The study uses a methodology that combines the MOGAII Genetic Algorithm (GA) and Computational Fluid Dynamics (CFD) to generate a novel optimized diffuser profile out of nearly 200 geometric shapes. A case for each of the bare turbine and the diffuser-augmented turbine were modeled using 3-dimensional Reynolds Averaged Navier Stokes equations (RANS) using k-ω SST model and FLUENT solver. The performance comparison indicated an overall average rise of 28.83% in power coefficient in favor of the diffuser-augmented case. The most significant performance rise of 47.19% was found at the low-speed region corresponding to Tip Speed Ratios between 8 and 12. Through investigating the starting torque at extremely low speeds above 1 m/s, it was evaluated that the starting torque increases significantly with an average rise of 60.64% in favor of the diffuser-augmented case, the enhanced starting torque widened the operational range of the wind turbine in the low wind speed region, and reduced the lowest required speed to induce a positive moment coefficient. This significant rise in performance particularly for low wind speeds which are dominantly more frequent in the annual wind conditions, combined with the enhanced starting capabilities, resulted in an annual generated energy increase of 23.18% compared to the bare turbine.
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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 | 0.000 |
| 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) | 0.001 | 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".