An enhanced simplified modeling approach for axial-flow and cross-flowhydrokinetic turbine arrays
Bibliographic record
Abstract
For the purpose of maximizing the energy extraction, it is essential to optimize the hydrokinetic turbine arrangement in farms using reliable numerical tools which allow affordable and realistic performance prediction for multiple turbines in consideration of the interactions between them.The Effective Performance Turbine Model (EPTM), an actuator region model used in 3-D Reynolds-Averaged Navier-Stokes simulations, has been designed for both axial-flow turbines (AFT) and cross-flow turbines (CFT).This simplified model, representing each individual turbine with non-uniform momentum source terms scaled with the local velocity, has proven to be reliable in predicting the mean performance of each turbine in the array as well as to reproduce realistic wakes for the turbines operating at their optimal point.However, this was demonstrated in uniform and clean flow conditions.An important remaining challenge consists in the turbulence modeling within realistic perturbed flow conditions in farms.Since the EPTM does not generate the actual discrete vortex system by its steady nature, its wake may suffer from lower turbulence level compared to the fully-resolved unsteady turbine wake.Especially for the AFT technology for which the wake dynamics is mostly governed by an instability of its vortex structure, thus quite sensitive to the flow perturbations in presence, the underestimated turbulence production decelerates the wake recovery and further affects the performance prediction of downstream turbines.In this work, the models EPTM-AFT and EPTM-CFT are modified with additional turbulence source terms to compensate the turbulence induced by the unsteady phenomena.Several turbine array configurations are tested to illustrate the capability of the enhanced models to operate in perturbed flow conditions and to reproduce wakes well matching the actual wake characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".