Learning to diagnose leading edge erosion degradation on an airfoil via aerodynamic pressure coefficients
Creators: name: Duthé, Gregory
name: Abdallah, Imad
name: Marykovskiy, Yuriy
name: Barber, Sarah
name: Chatzi, Eleni
Publisher: name: ETH Zurich
Year: 2021

description: Identifying the extent of erosion on the leading edge of wind turbine blades is becoming increasingly important in the wind energy industry as blades become larger and more flexible. Leading edge erosion (LEE) can result from abrasive airborne particles or weather conditions, and can impact the Annual Energy Production of a MW-scale wind turbine on the order of 5% (Langel et al., 2015). Current methods for identifying LEE involve manual (Nielsen et al., 2020) or drone-based visual inspection (Shihavuddin et al., 2019), electrical signal analysis (He et al., 2020) or vibration monitoring (Skrimpas et al., 2016) , methods which either require the turbine to be shut down or are limited for continuous monitoring (Du et al., 2020). In this work, we propose a data-driven model to predict the state of degradation of the leading edge of a 2D airfoil via aerodynamic pressure coefficient learning, under the influence of various uncertain inputs and parameters. The learning-based algorithm is trained on a novel dataset, comprised of pressure coefficient distributions that are generated by 2D steady-state Computational Fluid Dynamics (CFD) simulations. Each individual simulation is executed with a different set of input parameters, constructed via probabilistic sampling, with distributions designed to mimic operational conditions of a wind turbine.

The airfoil LEE status can be broadly sorted into four categories corresponding to the severity of the degradation (Sareen et al., 2014): (1) undamaged, (2) presence of pits, (3) presence of pits and gouges and (4) presence of pits, gouges, and delamination. This categorization forms the prediction goal of the learning algorithm. In our CFD setup, which is based on the k-Omega SST Reynolds-averaged Navier–Stokes turbulence model using OpenFOAM, the surface conditions of the leading edge are emulated via the use of rough wall-functions, based on previous work by (Knopp et al., 2009). By adjusting the sand-grain roughness and the spatial extent of the rough patch, we can simulate all degradation categories. We validate this approach by comparing our simulation results in terms of lift, drag and pressure coefficients to experimental data and CFD simulation data from the literature (Maniaci et al., 2016).

We aim to train a model which is robust to uncertain conditions and which can generalize to any type of airfoil. This requires a dataset established across a wide range of airfoil shapes and flow conditions. To increase robustness to shape warping induced by blade deflections, we propose a novel generative learning method to produce unique, random yet coherent, airfoil geometries, which are subsequently used as the basis for the CFD simulation meshes. As a result, during training, the learning algorithm is never exposed to multiple samples with the exact same airfoil geometry which allows for better generalization. By using random probabilistic sampling to draw the airfoil geometry and the other CFD flow condition inputs, we create an ensemble of unique and diverse pressure coefficient curves

The deep learning algorithm we propose admits as inputs the pressure coefficient distribution and geometry of an airfoil, and outputs one of four degradation labels. A neural network is chosen as the data-driven model, although we test and compare multiple different network types and architectures. We show classification scores for all models and discuss their suitability for the task at hand, as well as potential improvements.