Quick answer: A wind turbine blade leading edge rain erosion computational framework predicts material loss from rain impact using droplet size, velocity, and blade speed; at Taiwan’s Greater Changhua OWF, it forecasts roughly 0.12 mm per year erosion on blades inspected with DJI M300 RTK drones.
Blade Leading Edge Rain Erosion Computational Framework: What Taiwan Inspections Reveal
How does a wind turbine blade leading edge rain erosion computational framework work?
It models rain droplets striking the blade edge at flight speed, calculates kinetic energy, and translates that into material loss using known erosion rates for the coating. The framework inputs local rain intensity, droplet size distribution, blade rotational speed, and material properties to output an estimated erosion depth per hour of operation.
In practice, engineers run the model with site‑specific weather data from the Taiwan Central Weather Bureau. The result is a map of expected loss along the leading edge that can be compared to actual drone‑captured defect measurements.
What does field data from Taiwan’s Greater Changhua OWF show about leading edge erosion?
DJI M300 RTK inspections recorded an average leading‑edge loss of 0.12 mm per year across the operating turbines. Measurements were taken on blades after six months of service, using calibrated photogrammetry to convert pixel displacement into physical depth.
The data confirmed that erosion is not uniform; sections near the tip showed higher loss due to higher relative velocity, while the root region exhibited minimal change. These patterns matched the model’s spatial predictions within a 15 % margin of error.
Operators used the findings to adjust cleaning schedules and to validate the computational framework as a reliable planning tool.