This study introduces a method for detecting wind turbine blade erosion by approximating the outputs from aerodynamic simulations with a Gaussian process emulator. Once trained, querying the emulator is essentially free, which enables the efficient generation of datasets for training damage classification models. This framework could provide an essential building block for constructing a digital twin for wind turbine condition monitoring.