RANS Simulation of two co-axially positioned HAWT under different thermal stratification conditions
Bibliographic record
Abstract
This paper proposes numerical method for aerodynamic performance predictions of Horizontal Axis Wind Turbines (HAWTs) immersed on Atmospheric Boundary Layer (ABL) flows under unstable, stable and neutral conditions. The flow field has been described using the three-dimensional Reynolds Averaged Navier Stokes (RANS) equations and the k-ϵ turbulence model with modified constants corresponding to ABL flows is considered. Based on Monin-Obukhov similarity, the ABL profiles under neutral and stratified conditions have been implemented into OpenFOAM where the flow over the fetch has been calculated through an in-house solver (ABLSolversimpleFoam) developed for steady-state turbulent flows. The immersed HAWT has been modeled using the actuator disk approach coupled to the Blade Element theory for the loads estimation. The obtained results have indicated that the velocity defect in the wake of a wind turbine is more significant in unstable than in stable thermal stability conditions. Consequently, significant variation of the wind turbine performances have been noticed; a power output drop of 37.64% and 13.67% being recorded for the second rotor placed in the wake of another and the first one respectively.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".