Experimental study of the impacts of porous plates on steady flow velocities for hydrokinetic energy resource impact assessment applications
Bibliographic record
Abstract
In order to take advantage of economies of scale, hydrokinetic energy (HKE) developers typically deploy multiple turbines within rivers or the marine environment in array or farm configurations. Successful planning and design of turbine array deployments requires an understanding of turbine wake hydrodynamics, wake interactions within arrays, and the performance of turbines within array fields, to enable quantification of the extractable power and impacts on the surrounding environment. However, consistent and reliable methods for predicting the power generation capabilities of turbine arrays and the total extractable power from a given site remain elusive. Numerical hydrodynamic models show considerable promise as tools to support hydrokinetic energy resource assessment, turbine array site selection, array design and impact assessment. For example, Computational Fluid Dynamics (CFD) models provide a means to analyse the high frequency motions and complex geometries associated with turbine-fluid interactions at the scale of individual turbines. CFD models can be integrated with numerical models that solve free surface flow equations to study the interactions between turbine arrays and hydrodynamics at coastal region or river reach scales. However, numerical models remain subject to limitations and require calibration and validation to provide confidence in their predictive capabilities, and to quantify uncertainty. This paper presents preliminary work whereby a set of large scale physical models was utilized to document the magnitude and spatial distribution of the velocity deficit at a high resolution upstream, and within the downstream wake, of simplified representations of cross-flow turbines modelled as porous rectangular plates. Subsequent phases of the research will include using this experimental data to calibrate and validate a CFD model of the porous plates, and conducting scale model experiments using more realistic, moving cross-flow hydrokinetic turbines to support improvements in CFD modelling techniques for HKE applications.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".