Efficiency-Driven Supervised Learning Regressors in Power Modeling and Optimization of Vertical Axis Wind Turbines
Bibliographic record
Abstract
Abstract Of all the clean energy sources, wind power stands as the most widely available and employed form. Besides the Horizontal Axis Wind Turbines (HAWT), Vertical Axis Wind Turbines (VAWT) are attracting significant attention. This study focuses on optimizing a 12-kW lift-based 3-blade VAWT by introducing an optimal diffuser to enhance performance using two optimization procedures. To do that, a nonsymmetric diffuser in the shape of NACA4405 is introduced to the VAWT which is simulated using Computational Fluid Dynamics (CFD) and validated with experimental data. To optimize the power coefficient (Cp) of the VAWT, adjustments will be made to the position and orientation of both the upper and lower walls of diffuser. Two optimization methods were employed: one involves direct optimization, where a Genetic Algorithm (GA) is integrated with CFD. The second method utilizes Machine Learning (ML) models, such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and AdaBoost, fitted to the data set extracted from CFD. These ML models then replace CFD and are used in optimization. Results show that optimal diffuser can contribute to 27% increase in Cp. When employing GPR and AdaBoost, the optimal Cp values closely match those from direct optimization while optimization using ML models reduces computational costs by nearly 78% fewer simulation runs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".