Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand
Bibliographic record
Abstract
Situated in the southern part and on the western coast of Thailand, the Andaman Coast covers the provinces of Ranong, Phangnga, Phuket, Krabi, Trang and Satun. Using a coupled mesoscale atmospheric model and a microscale wind flow model, along with computational fluid dynamics (CFD) modeling, this paper presents a detailed assessment of the wind energy potential for power generation along the Andaman Coast of Thailand. The climatic data are obtained from the Modern Era Retrospective analysis for Research and Applications (MERRA), along with a high-resolution topography database and Land Use Land Cover digital data. The results are compared to the equivalent wind speeds obtained with the Weather Research and Forecasting (WRF) atmospheric model. The results showed that, at 120 m above ground level (agl), the predicted wind speeds from the models proposed were 20% lower for the mesoscale model and 10% lower for the microscale model when compared to the equivalent wind speeds obtained from the WRF model. A CFD wind flow model was then used to investigate 3D wind fields at 120–125 m agl over five potential sites offering promising wind resources. The annual energy productions (AEP) and the capacity factors under three different wake loss models and for five wind turbine generator technologies were optimized for 10-MW wind power plants, as per Thailand’s energy policies. With capacity factors ranging from 20 to 40%, it was found that the AEPs of the best sites were in the range of 18–36 GWh/year, with a total AEP in the vicinity of 135 GWh/year when using a single wind turbine model for the five sites studied. The combined energy productions by these wind power plants, once operational, could avoid GHG emissions of more than 80 ktons of CO2eq/year.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".