Meta-Modeling Based Optimization of a Directional Augmentation Technique on Improving the Performance of Six Blades Savonius Rotor Using CFD Analysis
Bibliographic record
Abstract
The performance of Savonius wind turbine can be improved by increasing the effective wind velocity. One of the methods of improving the effective wind velocity is using directional augmentation technique, which actually affects the Omnidirectional capability of the Savonius rotor. This paper works on this method by using convergent nozzle at the outlet of the rotor. The whole work is based on Metamodeling based optimization and numerical simulation. Reynolds averaged Navier-stokes equation (RANS) based turbulence model has been used for simulations, such as static simulation and dynamic simulation. The CFD simulations are validated against previously published experimental data. The optimization procedure is performed by integrating the Design of Experiment (DOE), Computational Fluid Dynamics (CFD), Response Surface Model (RSM) and analysis of variance (ANOVA). The meta-model is able to identify significant design variable and the interactions. The proposed optimal nozzle is shown to improve the coefficient of the moment from 0.3 to 0.44.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".