TY - JOUR

T1 - Model updating of wind turbine blade cross sections with invertible neural networks

AU - Noever Castelos, Pablo

AU - Ardizzone, Lynton

AU - Balzani, Claudio

N1 - Funding Information: This work was supported by the compute cluster, which is funded by the Leibniz University Hannover, the Lower Saxony Ministry of Science and Culture (MWK), and the German Research Foundation (DFG). This work was also supported by the Federal Ministry for Economic Affairs and Energy of Germany (BMWi) projects SmartBlades2 (grant number 0324032C) and ReliaBlade (grant number 0324335B).

PY - 2022/3/10

Y1 - 2022/3/10

N2 - Fabricated wind turbine blades have unavoidable deviations from their designs due to imperfections in the manufacturing processes. Model updating is a common approach to enhance model predictions and therefore improve the numerical blade design accuracy compared to the built blade. An updated model can provide a basis for a digital twin of the rotor blade including the manufacturing deviations. Classical optimization algorithms, most often combined with reduced order or surrogate models, represent the state of the art in structural model updating. However, these deterministic methods suffer from high computational costs and a missing probabilistic evaluation. This feasibility study approaches the model updating task by inverting the model through the application of invertible neural networks, which allow for inferring a posterior distribution of the input parameters from given output parameters, without costly optimization or sampling algorithms. In our use case, rotor blade cross sections are updated to match given cross-sectional parameters. To this end, a sensitivity analysis of the input (material properties or layup locations) and output parameters (such as stiffness and mass matrix entries) first selects relevant features in advance to then set up and train the invertible neural network. The trained network predicts with outstanding accuracy most of the selected cross-sectional input parameters for different radial positions; that is, the posterior distribution of these parameters shows a narrow width. At the same time, it identifies some parameters that are hard to recover accurately or contain intrinsic ambiguities. Hence, we demonstrate that invertible neural networks are highly capable for structural model updating.

AB - Fabricated wind turbine blades have unavoidable deviations from their designs due to imperfections in the manufacturing processes. Model updating is a common approach to enhance model predictions and therefore improve the numerical blade design accuracy compared to the built blade. An updated model can provide a basis for a digital twin of the rotor blade including the manufacturing deviations. Classical optimization algorithms, most often combined with reduced order or surrogate models, represent the state of the art in structural model updating. However, these deterministic methods suffer from high computational costs and a missing probabilistic evaluation. This feasibility study approaches the model updating task by inverting the model through the application of invertible neural networks, which allow for inferring a posterior distribution of the input parameters from given output parameters, without costly optimization or sampling algorithms. In our use case, rotor blade cross sections are updated to match given cross-sectional parameters. To this end, a sensitivity analysis of the input (material properties or layup locations) and output parameters (such as stiffness and mass matrix entries) first selects relevant features in advance to then set up and train the invertible neural network. The trained network predicts with outstanding accuracy most of the selected cross-sectional input parameters for different radial positions; that is, the posterior distribution of these parameters shows a narrow width. At the same time, it identifies some parameters that are hard to recover accurately or contain intrinsic ambiguities. Hence, we demonstrate that invertible neural networks are highly capable for structural model updating.

KW - Bayesian optimization

KW - blade cross section

KW - invertible neural network

KW - machine learning

KW - model updating

KW - sensitivity analysis

KW - wind turbine rotor blade

UR - http://www.scopus.com/inward/record.url?scp=85117373982&partnerID=8YFLogxK

U2 - 10.1002/we.2687

DO - 10.1002/we.2687

M3 - Article

AN - SCOPUS:85117373982

VL - 25

SP - 573

EP - 599

JO - WIND ENERGY

JF - WIND ENERGY

SN - 1095-4244

IS - 3

ER -