Downstream tidal turbine transient local blade loading characterization
Bibliographic record
Abstract
Flow perturbations carried in the wake of an upstream turbine can have a significant impact on the local and transient loads observed on the downstream one. To get a better understanding of the effect of unsteady asymmetric flow on the load felt by a downstream turbine and develop a method to extract local and transient blade loading from CFD results, fully transient simulations designed to study this effect were performed with a RANS k-ω SST turbulence model using ANSYS-CFX. A horizontal axis tidal turbine (HATT) was used for the study. Three configurations were considered: the downstream turbine aligned with the upstream one, the downstream turbine offset by 0.5 D and finally offset by 1 D , with D being the diameter of the turbine. A 10 D clearance between both turbines was used. Results show that when fully in-line, the downstream turbine sees reduction in power coefficient by almost 70 %, with a temporal variation of this coefficient having a relative amplitude of more than 30 %. Furthermore, the blades see localized loading varying by a factor of up to 2 during their rotation and the changes in the load amplitude applied at the same location are varying by more than 13 %. Blade load and flap-wise bending moment display significant amplitude variations for the 0 D and 0.5 D offsets, with values 8 and 12 times higher to what is observed for the 1 D offset. • Transient characterization of a downstream turbine subjected to the wake of an upstream turbine was performed. • Variations in global downstream turbine coefficients of performance and thrust were seen to vary greatly. • Presentation ofmethodology to obtain local blade thrust coefficients that vary by up to 100% locally during bladerotations.
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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".