Straight-bladed vertical axis wind turbine rotor design guide based on aerodynamic performance and loading analysis
Bibliographic record
Abstract
Vertical axis wind turbines with straight blades are attractive for their relatively simple structure and aerodynamic performance. An efficient design methodology is required to enhance this resurging renewable energy technology. This paper aims to provide a robust design procedure built on an existing analytical approach to determine the optimum range of the design parameters for prototype construction. Identifying the proper range of design parameters can save significant time and resources in the initial turbine development stages. Here the double-multiple streamtube method has been utilized to analyze turbine aerodynamic performance. A parametric optimization has been performed for several design factors to maximize the turbine power coefficient and its operational range. The results show that the optimum value of the rotor solidity factor, blade aspect ratio, and rotor aspect ratio are in the range of 0.2 < σ < 0.6, 10 < μ < 20, and 0.5 < H/ D < 2, respectively. Aerodynamic loading analysis has also been carried out, and the most severe stresses acting on the blades and supporting arms were determined. The most favorable bending stress distribution along the blade occurred when two supporting arms per blade were used at intermediate locations of 21% and 79% along the blade length. A comparative study of different supporting arm shapes demonstrated that utilizing aerodynamic profiles for turbine arms created the most acceptable aerodynamic response. A summary of design aspects addressed in this paper is presented in a useful summary flowchart.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".