Analysis of Transient Inlet Velocity Impacts on Hydrodynamic Performance, Blade Loading, and Wake Dynamics on a Horizontal Axis Tidal Turbine using Detached Eddy Simulation
Bibliographic record
Abstract
This study investigates the effects of transient inlet velocity on the hydrodynamic performance, blade loading, and wake dynamics of a three-bladed horizontal axis tidal turbine (HATT). The turbine geometry corresponds to that used in experimental studies at IFREMER, enabling a robust comparison with empirical data. Advanced computational fluid dynamics (CFD) simulations use the Improved Delayed Detached Eddy Simulation (IDDES) turbulence model within ANSYS FLUENT to capture the complex unsteady flow phenomena inherent to tidal energy systems. The turbulence closure model utilized, Detached Eddy Simulation (DES), combines the strengths of the k-ω Shear Stress Transport (SST) model and Large Eddy Simulation (LES). The k-ω SST model is effective at resolving flow in near-wall regions, capturing small-scale turbulent structures, while LES is employed to accurately model large, anisotropic turbulent eddies in the outer flow domain. This hybrid approach ensures a comprehensive representation of the multiscale turbulence dynamics encountered by tidal turbines. A time-dependent velocity profile is imposed at the inlet boundary, representing the time-varying characteristics of turbulent tidal flows. This transient velocity captures realistic velocity fluctuations, offering an accurate simulation of the transient hydrodynamic environment. A sliding mesh technique is used to simulate the rotational motion of the turbine blades, allowing precise analysis of blade-flow interactions and the associated unsteady hydrodynamic loading. The study provides detailed analyses of transient forces on turbine blades, including pressure and viscous contributions, through force monitoring and surface integration techniques. Sectional load distributions are investigated to identify periodic variations in blade loading, revealing insights into the impact of unsteady flow on the hydrodynamic performance of tidal turbines. Furthermore, wake dynamics are investigated to characterize velocity deficits and turbulence intensity downstream, which are critical for understanding turbine spacing and array design. The simulation results are compared with those obtained from Reynolds-Averaged Navier-Stokes (RANS) simulations using the k-ω SST model and experimental data from IFREMER. The comparisons highlight the superiority of DES in resolving unsteady flow features and provide validation for the numerical model. In conclusion, this study offers a comprehensive understanding of the transient hydrodynamic effects on tidal turbine performance, advancing the optimization of blade design and operational strategies. The findings have significant implications for improving the reliability and efficiency of tidal energy systems in real-world operating conditions.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".