The design of cost-effective and energy-efficient wind turbine blades still represents a major technological challenge in a highly competitive market environment. A key driver in this challenge is the need for improved energy production rates achieved through increasingly longer blades and a reduced weight-to-strength ratio. Over the last few decades, blade manufacturers have addressed this issue by relying on complex composite materials, requiring extensive tests before deployment.
The high cost and long duration of physical tests typically allows to conduct only a few design iterations. To target optimal designs, manufacturers need to resort to numerical simulations which reflect the real-world behaviour of the blades. Due to the high material complexity and the various physical effects to be modeled, these simulations are rarely conducted with high accuracy at full scale, leaving large parts of the design space unexplored.
One of the main obstacles in performing accurate numerical simulations of blades in an industrial scale is the large number of parameters involved in the models. Not only does a composite layup easily possess hundreds or even thousands of different layers, but this setup also varies in each segment of the blade. While composites are explored thanks to their favorable properties in terms of weight and strength, the improvement of the aerodynamic shape of the blade is crucial to optimally capture the kinetic energy from the wind. This alone easily generates a very high-dimensional design space which is difficult to explore in detail without very targeted tools.
A standard tool to analyse the physical behaviour of a blade numerically is finite element analysis (FEA). An accurate numerical model of a blade may easily possess millions of degrees of freedom in a single design iteration. Due to the resulting high computational cost, this has limited the number of design cycles conducted numerically. Digital Twin pioneer Akselos addresses this issue with its patented Reduced Basis FEA technology. Heavily reducing the time for a single large-scale structural simulation to seconds, Akselos uses parameterised components to include design variations into its models. For the first time, this allows for detailed design parameter studies in a truly industrial scale model setup.
While manufacturers seek to optimize next-generation wind turbine blades, operators of wind farms aim at running their existing portfolio of turbines as efficiently as possible. Most notably, the over-conservative blade design of the past has brought up the question whether turbines can safely be operated beyond their originally envisioned design period of twenty years. To quantify this question numerically, detailed fatigue analysis studies on the blades need to be conducted.
Akselos Digital Twins take the true load history of the blades into account which helps operators to make informed decisions for their life extension programmes. This allows them to unlock spare capacities, leading to reduced energy costs.
By Jonas Ballani, Phd, core technology developer