Physics-informed deep convolutional hierarchical encoder-decoder neural network for flow field prediction in wind farms
Bibliographic record
Abstract
Wind Farm Layout Optimization (WFLO) is a critical step in wind farm design, focusing on determining the optimal placement of turbines to maximize the annual energy production (AEP) of wind farms. Calculating AEP as an objective function in WFLO often relies on computationally expensive computational fluid dynamics (CFD) simulations to calculate the flow field within the farm. In this study, we propose PI-DeepWFLO, a physics-informed deep convolutional hierarchical encoder-decoder neural network, as a surrogate model to predict flow fields for various turbine configurations, significantly reducing dependence on costly CFD simulations. PI-DeepWFLO is trained on labeled data using a customized physics-informed loss function that incorporates mass and momentum conservation laws. Our results show that the proposed PI-DeepWFLO accurately predicts spanwise and streamwise velocity fields ( R 2 = 0.955 ), effectively capturing wake interactions between turbines. Furthermore, results show that PI-DeepWFLO is less sensitive to variations in network weight initialization and training datasets than purely data-driven alternatives, exhibiting a ten-fold lower R 2 variance over different re-samplings of the training dataset. A comparison of AEP values calculated from PI-DeepWFLO and CFD-generated flow fields demonstrates a median error of 1.25% across test cases. Importantly, the Spearman’s Rank Correlation Coefficient between AEPs from CFD and PI-DeepWFLO flow fields is 1.0, confirming the PI-DeepWFLO’s suitability for AEP estimation in optimization studies. We illustrate PI-DeepWFLO’s performance in an application context by employing it as a surrogate model for a WFLO task.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".