Novel interpretable wake model using Machine Learning based symbolic regression for wind turbines
Bibliographic record
Abstract
The escalating demand for energy, dwindling fossil fuel reserves, shifting fuel prices, and worries about climate change and pollution have prompted the investigation and assessment of renewable energy sources. Among these, wind power has emerged as a particularly promising candidate. However, due to limited suitable locations, wind turbines are often clustered together, necessitating the study of wake aerodynamics to optimize wind farm performance. Understanding and predicting wake characteristics that includes decreased velocity deficit and increased turbulence intensity are crucial for improving the efficiency of downstream turbines. Traditionally, low-fidelity models were used to predict wake characteristics, but recent advancements in computational fluid dynamics (CFD) have enabled more accurate predictions, albeit at the cost of increased computational time and resources. Machine learning (ML) has proven to be a valuable tool in addressing complex problems in various engineering domains, leading to the development of ML-based wake models that leverage CFD data for rapid predictions. However, these models have been limited by their reliance on a single wind turbine’s CFD results and their lack of generalizability to other turbines with different blade geometries and operating conditions. In response to these limitations, this study presents a novel wake prediction model using the symbolic regression technique. This innovative approach aims to offer precise aerodynamic performance predictions while maintaining simplicity, interpretability, and generalizability across various wind turbine designs. The proposed model holds significant potential for enhancing wind farm performance by accurately predicting wake characteristics and facilitating better decision-making in turbine placement and operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".