Tidal and wind turbine simulation with the simulation code DOROTHY

Bex, C. Choma; Dufour, M.-A.; Belkacem, Y. Ben; Pinon, G.; Germain, G.; Rivoalen, E.

doi:10.1201/9781003360773-14

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…LOMC-Vortex contributed by LOMC employs a 3D unsteady Lagrangian Vortex Particle Method code, Dorothy, which represents the flow as a system of vorticity carrying particles [38]. The code can account for turbulent inflow and multi-wake interaction [39]. A lifting line model represents the blades, with tabulated lift and drag coefficients, together with blade-induced velocities from the previous time-step.…”

Section: Other Approachesmentioning

confidence: 99%

Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions

Willden,

Chen,

Tucker Harvey

et al. 2023

Proc. EWTEC

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This paper presents the first blind prediction stage of the Tidal Turbine Benchmarking Project being conducted and funded by the UK's EPSRC and Supergen ORE Hub. In this first stage, only steady flow conditions, at low and elevated turbulence (3.1%) levels, were considered. Prior to the blind prediction stage, a large laboratory scale experiment was conducted in which a highly instrumented 1.6m diameter tidal rotor was towed through a large towing tank in well-defined flow conditions with and without an upstream turbulence grid. Details of the test campaign and rotor design were released as part of this community blind prediction exercise. Participants were invited to use a range of engineering modelling approaches to simulate the performance and loads of the turbine. 26 submissions were received from 12 groups from across academia and industry using solution techniques ranging from blade resolved computational fluid dynamics through actuator line, boundary integral element methods, vortex methods to engineering Blade Element Momentum methods. The comparisons between experiments and blind predictions were extremely positive helping to provide validation and uncertainty estimates for the models, but also validating the experimental tests themselves. The exercise demonstrated that the experimental turbine data provides a robust data set against which researchers and design engineers can test their models and implementations to ensure robustness in their processes, helping to reduce uncertainty and provide increased confidence in engineering processes. Furthermore, the data set provides the basis by which modellers can evaluate and refine approaches.

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Section: Other Approachesmentioning

confidence: 99%

Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions

Willden,

Chen,

Tucker Harvey

et al. 2023

Proc. EWTEC

View full text Add to dashboard Cite

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Efficient prediction of tidal turbine fatigue loading using turbulent onset flow from Large Eddy Simulations

Mullings,

Amos,

Miller

et al. 2024

J. Ocean Eng. Mar. Energy

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To maximise the availability of power extraction from a tidal stream site, tidal turbines need to be able to operate reliably when located within arrays. This requires a thorough understanding of the operating conditions, which include turbulence, velocity shear due to bed proximity and roughness, ocean waves and due to upstream turbine wakes, over the range of flow speeds that contribute to the loading experienced by the devices. High-fidelity models such as Large Eddy Simulation (LES) can be used to represent these complex flow conditions and turbine device models can be embedded to predict loading. However, to inform micro-siting of multiple turbines with an array, the computational cost of performing multiple simulations of this type is impractical. Unsteady onset conditions can be generated from the LES to be used in an offline coupling fashion as input to lower-fidelity load prediction models to enable computationally efficient array design. In this study, an in-house Blade Element Momentum (BEM) method is assessed for prediction of the unsteady loads on the turbines of a floating tidal device with unsteady inflow developed with the in-house LES solver DOFAS. Load predictions are compared to those obtained using the same unsteady inflow to the commercial tool Tidal Bladed and from an Actuator Line Model (ALM) embedded in the LES solver. Estimates of fatigue loads differ by up to 3% for mean thrust and 11% for blade root bending moment for a turbine subject to a turbulent channel flow. When subjected to more complex flows typical of a turbine wake, the predictions of rotor thrust fatigue differ by up to 10%, with loads reduced by the inclusion of a pitch controller.

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Tidal and wind turbine simulation with the simulation code DOROTHY

Cited by 2 publications

References 21 publications

Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions

Tidal Turbine Benchmarking Project: Stage I - Steady Flow Blind Predictions

Efficient prediction of tidal turbine fatigue loading using turbulent onset flow from Large Eddy Simulations

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