Improving the K-ω SST Turbulence Model to Perform the Numerical Calculation for the Darrieus Vertical-Axis Wind Turbine Profiles
Bibliographic record
Abstract
The choice of suitable airfoils in wind turbine rotors is known as an important task to appropriately increase the blare sections’ lift without significant increase in their drags. In fact, the configuration should reduce the chance of flow separation at airfoil sections while boosting the turbine efficiency. So, one important task is to accurately predict the flow field around the rotor’s sections. Experiences have shown that the complexities of such flow fields require using more accurate turbulence models such as the k-ω SST model. However, the classical k-ω SST model has not been developed specifically for treating the flow fields around the wind turbine rotors. So, the current question is whether it is possible to improve the accuracy of existing k-ω SST model in specific fluid flow applications such as the wind turbine flow field predictions. This study focuses on improving the accuracy of the k-ω SST turbulence model in the computational fluid dynamics simulation of the DU06-W-200 airfoil section, which has wide applications in wind turbine applications including the Darrieus vertical-axis wind turbine case. Definitely, the accurate aerodynamic predictions are essential to choose the most suitable section, where they result in optimum turbine performance. To improve the accuracy of predictions, this work tries to improve the accuracy of the classical k-ω SST turbulence model via an optimization procedure, where its design parameters are consisted of the constants of the k-ω SST turbulence model. The essence of the defined objective function is to quantify the differences between the numerical solutions and measured data. The measured inaccuracy, defined by the objective function, is suitably minimized using an appropriate minimization technique. Ultimately, the developed optimization algorithm arrives at new empirical constants, whose values differ from those of the classical method constants, which are commonly used in the standard k-ω SST model. The modified k-ω SST turbulence model is then applied to solve a 2D-DU airfoil section. This airfoil section is chosen because there are accurate experimental data to construct the required objective function. The investigation is performed at three Reynolds numbers and seven angles of attack. The present research shows that the improved k-ω SST turbulence model significantly improves the accuracy of numerical predictions. Compared to the measured data, the new improved model shows higher accuracies than the classical one in predicting different aerodynamic predictions such as lift, drag, and the lift-to-drag ratio.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".