What are the latest CFD innovations for optimizing wind turbine design?

1 Adaptive Meshing

One of the key aspects of CFD is the discretization of the fluid domain into a mesh of cells, which determines the resolution and accuracy of the solution. However, creating a suitable mesh for a wind turbine problem can be challenging, as it needs to capture the large-scale geometry, the small-scale boundary layer, and the dynamic flow features. Adaptive meshing is a technique that allows the mesh to adjust itself during the simulation, based on criteria such as flow gradients, errors, or user-defined regions of interest. This way, the mesh can refine or coarsen as needed, reducing the computational cost and improving the solution quality.

2 Multiphysics Coupling

Another challenge of CFD is to account for the interactions between different physical phenomena that affect the wind turbine performance. For example, the wind loads can cause the blades to deform, which in turn can alter the aerodynamics and the structural stresses. Similarly, the rotation of the blades can generate noise, which can affect the acoustic environment and the human perception. Multiphysics coupling is a technique that enables the communication and exchange of data between different CFD models or software, such as structural, acoustic, or thermal analysis. This way, the multiphysics coupling can capture the feedback and coupling effects between the various physical domains, leading to more realistic and comprehensive results.

3 Machine Learning

A third challenge of CFD is to deal with the uncertainty and variability of the wind conditions and the wind turbine operation. For example, the wind speed, direction, and turbulence can change rapidly and unpredictably, affecting the power output and the load fluctuations. Similarly, the wind turbine settings, such as pitch angle, yaw angle, or tip speed ratio, can vary depending on the control strategy and the optimization objectives. Machine learning is a technique that uses data-driven algorithms to learn from existing data and make predictions or recommendations for new situations. For example, machine learning can be used to train surrogate models that can approximate the CFD results with less computational effort, or to design optimal control strategies that can maximize the power production and minimize the fatigue damage.

4 Cloud Computing

A final challenge of CFD is to access and manage the high-performance computing (HPC) resources that are required to run complex and large-scale simulations. For example, a typical CFD simulation of a wind turbine blade can involve millions of cells and thousands of time steps, requiring hours or days of processing time on a powerful workstation or cluster. Cloud computing is a technique that offers on-demand and scalable access to HPC resources over the internet, such as servers, storage, or software. For example, cloud computing can be used to run parallel or distributed simulations, to store and share large amounts of data, or to access specialized CFD software and platforms.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?