Some numerical recommendations for cavitation simulations of cross-flowhydrokinetic turbines
Bibliographic record
Abstract
Cavitation has detrimental effects on the hydrodynamic and structural performances of hydraulic machines including tidal current turbines and river hydrokinetic turbines.To-date, cavitation studies investigating the inception conditions and the impact of cavitation on these technologies are still very scarce and need further effort.The associated inception and development mechanisms, and how they exactly affect the turbines' performance are largely unknown.This work presents a numerical validation of attached cavitation using Star-CCM+ and CFX finite volume softwares.Cavitation is often modeled with the use of an interface capturing method such as the volume of fluid approach in numerical simulations.This interface capturing method coupled with a cavitation model is widely used in commercial softwares.The two different cavitation models offered in Star-CCM+ and CFX are the full Rayleigh-Plesset and a simplified version called the Schnerr-Sauer model.The present paper aims to clarify and identify the differences between both models used in situations where an hydrofoil is oscillating and experiencing unsteady hydrodynamics as it would in a cross-flow turbine.The numerical results are compared and validated to a previous experimental work on a modified NACA 009 truncated profile for various Reynolds and cavitation numbers.The cavitation length, the pressure coefficient distribution and the hydrodynamic force coefficients are compared to the experimental data.A second comparison is made with another experimental work that investigated an oscillating NACA 16-012 in cavitating conditions.This second experimental work is more representative of the unsteady hydrodynamics experienced by the cross-flow turbine blades throughout a cycle.The present paper discusses the numerical recommendations on the spatial and temporal discretizations as well as the software's convergence criteria.These recommendations will be used in future works for simulating hydrokinetic turbines in tidal currents and rivers under cavitating conditions to determine efficient ways to prevent cavitation or mitigate its negative impact.
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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".