A Numerical Investigation of Dual-Rotor Horizontal Axis Wind Turbines Using an In-House Vortex Filament Code (DR_HAWT)
Bibliographic record
Abstract
A numerical study was carried out using an in-house code named DR HAWT (Dual-Rotor Horizontal-Axis Wind Turbine code), to identify non-dimensional parameters for dual-rotor wind turbines (DRWTs).DR HAWT, which was implemented by the current author to predict the performance of single and dual-rotor wind turbines, uses a free vortex wake method with vortex filaments to represent the rotor and its wake.This vortex code was verified and validated using various blade-vortex interaction case studies and two well-known wind tunnel experiments (NREL Phase VI and MEXICO wind turbines).Based on some important DRWT parameters such as the rotor speeds, rotor diameters and the separation distance between the rotors, three dimensionless parameters were derived from the Buckingham Pi theorem.These three parameters define the ratio of diameters (diameter ratio, DR), the ratio of the separation distance and the downwind diameter (gap ratio, GR), and the ratio of the product of the upwind angular velocity and the downwind rotor radius and the oncoming wind velocity (combined tip-speed ratio, CTSR).In addition, the power output of each DRWT was normalized with the total power generated by equivalent single-rotor turbines.Hypothetical DRWT models were created using geometrically-scaled NREL Phase VI rotor geometry and operating conditions in order to confirm the validity of these First and foremost, I am very grateful to my supervisor, Dr. Edgar Matida, for giving me the opportunity to pursue graduate studies.Thank you for believing in me and providing support, guidance, and encouragement throughout these few years.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".