Machine learnt prediction method for rain erosion damage on wind turbine blades

This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given blade geometry, its coating material, and atmospheric conditions (wind rain) expected at installation site. Contrary what has been done so far, simulations (flow field particle tracking) are used not study specific operating condition but build large database of possible section. A machine learning algorithm, trained this database, defines prediction module that gives feature impact pattern over 2-D section, flow. The advantage is becomes much faster than using standard simulations; thus, set variable possible. module, coupled with an model, compute working In way, variations flow due dynamic effects such as wind, turbulence, control can be also considered computation. Here, we describe method, creation, development tool. Then, method applied section reference turbine, after one year operation rainy onshore Results good agreement observations, showing potential approach.