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Dr. Galih Bangga, a scientist with a forte in wind energy research, from the Institute of Aerodynamics and Gas Dynamics (IAG) at the University of Stuttgart, Germany, presented a paper where he discusses about the significance of automating mesh generation for complex designs of wind turbine blades for faster, flexible and accurate CFD solutions. The video presentation titled A High-Quality Automated Meshing Tool for Wind Turbine Blades is available at CadenceLIVE Europe 2021.
Wind Turbine blades have evolved from cloth-sailed wings to complex structures with intricate twists and turns, narrow airflow passages, snubbers, and cooling holes to provide top-class aerodynamic performance. These complicated geometries have made it difficult for CFD practitioners to study wind dynamics around the blades using traditional H-type, C-type, and O-type structured grids. Advanced grid discretization techniques are highly recommended to study flow separation around the trailing edges of the wind turbine blades.
The presenter covers various challenges in capturing the flow field surrounding the wind turbine rotor involving interactions of atmosphere and topography. Automating mesh generation by breaking down the CAD geometry into high-quality discrete cells can deliver accurate CFD predictions.
It is not a trivial task to generate high-quality mesh for aerodynamic or turbomachinery applications such as the blades of a wind turbine. Resolving vortex separation at the tip of the blade using Large Eddy Simulation (LES) or Detached Eddy Simulation (DES) is possible only using high-fidelity meshing technology. Moreover, achieving specific mesh requirements is a requisite for aerodynamic projections of various complex turbine blade models.
The boundary layer is the fluid layer that is near the surface of the respective body. As the flow transitions from laminar to turbulent, the thickness of the boundary layer tends to increase with Reynold’s number. During this transition to a fully developed turbulent flow, the viscous sublayer continues to occupy a fine layer as seen in Figure 1, and using Direct Numerical Simulation (DNS) for the solution comes with an increased computational expense, hence this layer needs to be modeled. A stronger velocity gradient in the turbulent flow necessitates a finer mesh to capture the near-wall behavior.
Figure 1. Boundary-Layer
Aspect ratio is a metric for measuring the quality of the mesh generated. It is the deviation of the mesh elements from having all sides of equal lengths. A desirable aspect ratio close to unity would produce a quadrilateral element of perfect shape as depicted in Figure 2. Having a lower aspect ratio will result in numerical dissipation and in turn reduce the accuracy of the CFD solution. The boundary layer domain is an exception, where a high aspect ratio is acceptable because the flow is physically dissipative.
Figure 2. Mesh Aspect Ratio
Skewness is another mesh metric that is often used by mesh analysts to monitor mesh quality. It can be defined as the difference in the shape of the generated mesh cell to an equilateral cell. The skewness metric ranges between 0 (best) and 1 (worst). As shown in Fig 3, cells with high skewness can cause discontinuity in velocity gradient and dissipation of energy from one cell to another, affecting the convergence rate and the accuracy of the solution.
Figure 3. Mesh Skewness
Several years of meshing experience and good mesh generation software are critical for high-quality mesh generation, but these requirements are not always available, and an alternative solution needs to be in place. While solving a CFD problem, about 80% of the entire solution hours are spent on building a high-quality mesh. A bad mesh increases the probability of an error in the simulation. Even with small changes in the geometry or cell parameters, the entire meshing process needs to be repeated and this can be tiresome.
Automating the mesh generation process can address the challenges in meshing complex geometries. It requires minimum user experience, provides faster and reliable solutions, is flexible with changes in geometry or mesh parameters, and provides comparable mesh quality for different meshes. The only limitation would be that the geometry should be comparable i.e. an aircraft wing cannot be meshed using the program that is scripted for turbine blade mesh generation.
The IAG group at the University of Stuttgart was successful in automating the mesh generation process for wind turbine blades using the Glyph scripting language in the Cadence Pointwise meshing software. As seen in Figure 4, the generated mesh was fully structured, had a fully resolved boundary layer with limited numerical dissipation that provided CFD solutions close to experimental results. Several parameters including the rotor flow, tip values, and wake generation input geometry were defined. Users have the flexibility to adjust the domain size, wake expansion growth rate, etc. using this program.
Figure 4. Wind Turbine Blades - Automated Mesh generation
Complex models of turbine blades require advanced grid discretization techniques for an accurate representation of the boundary layer and to capture the fluid-structure interaction. Having a fully resolved boundary layer with high-quality mesh metrics such as aspect ratio close to unity and low skewness can instill a deeper understanding of the near-wall behavior. Generating a fully automated mesh using the Glyph scripting language in Cadence Pointwise, can save time spend on meshing several models of comparable wind turbine blade geometries.
To learn more about Hybrid Meshing for Wind Turbine Applications, click here
In anticipation of the Cadence CFD webinars later this month, you can get a head start by requesting a demonstration of the Omnis CFD tools.