A numerical study on the interaction between two cross-flow turbines in tandem configuration to support a simplified turbine model approach
Bibliographic record
Abstract
In the planning of hydrokinetic turbine array deployment and for the performance prediction of its constituent turbines, the use of simplified turbine models is essential to alleviate the computational costs. The Effective Performance Turbine Model (EPTM) introduced in 2018 is a promising tool for that purpose, allowing array analysis and optimization. Its performance predictions scale with a local flow velocity characterization, which ensures to take into account inherently blockage effects and mean, local flow conditions. However, the characteristics of the local flow within an array also include different types of perturbation such as shear, large-scale temporal fluctuations, and turbulence. To ensure that the model is still reliable in those conditions, this paper presents a validation of the EPTM through the analysis of a large-scale tandem turbine configuration. In fact, three unsteady-Reynolds-averaged-Navier–Stokes simulations of a cross-flow turbine tandem configuration with a longitudinal spacing of six diameters have been conducted in this study, in addition to a simulation of a single turbine with turbulent ambient conditions. This set of simulations allows us to study independently the different types of perturbations associated with array deployment. Whereas the slow-varying large-scale upstream velocity fluctuations do not seem to affect significantly the turbine operation, the upstream non-uniform velocity distribution affects appreciably the extracted power. We find also that the value of the effective power coefficient associated with the EPTM needs to be adapted to the array simulation. Compared to the case of a single turbine in a uniform flow with a low turbulence level, we show that a smaller effective power coefficient value must be used in array simulations. An important result is that the different types of perturbations are found to yield similar effective performance coefficients, which suggests that the same set of values can be used throughout the array. With the appropriate set of values, we show that the EPTM succeeds to predict accurate downstream turbine performance.
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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".