TY - JOUR

T1 - Model updating of a wind turbine blade finite element Timoshenko beam model with invertible neural networks

AU - Noever-Castelos, Pablo

AU - Melcher, David

AU - Balzani, Claudio

N1 - Funding Information: Financial support. The publication of this article was funded by the open-access fund of Leibniz Universität Hannover. 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 Association (DFG). This work was supported by the Federal Ministry for Economic Affairs and Climate Action of Germany (BMWK) in the project ReliaBlade (grant number 0324335A/B).

PY - 2022/3/16

Y1 - 2022/3/16

N2 - Digitalization, especially in the form of a digital twin, is fast becoming a key instrument for the monitoring of a product’s life cycle from manufacturing to operation and maintenance and has recently been applied to wind turbine blades. Here, model updating plays an important role for digital twins, in the form of adjusting the model to best replicate the corresponding real-world counterpart. However, classical updating methods are generally limited to a reduced parameter space due to low computational efficiency. Moreover, these approaches most likely lack a probabilistic evaluation of the result. The purpose of this paper is to extend a previous feasibility study to a finite element Timoshenko beam model of a full blade for which the model updating process is conducted through the novel approach with invertible neural networks (INNs). This type of artificial neural network is trained to represent an inversion of the physical model, which in general is complex and non-linear. During the updating process, the inverse model is evaluated based on the target model’s modal responses. It then returns the posterior prediction for the input parameters. In advance, a global sensitivity study will reduce the parameter space to a significant subset on which the updating process will focus. The finally trained INN excellently predicts the input parameters’ posterior distributions of the proposed generic updating problem. Moreover, intrinsic model ambiguities, such as material densities of two closely located laminates, are correctly captured. A robustness analysis with noisy response reveals a few sensitive parameters, though most can still be recovered with equal accuracy. And, finally, after the resimulation analysis with the updated model, the modal response perfectly matches the target values. Thus, we successfully confirmed that INNs offer an extraordinary capability for structural model updating of even more complex and larger models of wind turbine blades.

AB - Digitalization, especially in the form of a digital twin, is fast becoming a key instrument for the monitoring of a product’s life cycle from manufacturing to operation and maintenance and has recently been applied to wind turbine blades. Here, model updating plays an important role for digital twins, in the form of adjusting the model to best replicate the corresponding real-world counterpart. However, classical updating methods are generally limited to a reduced parameter space due to low computational efficiency. Moreover, these approaches most likely lack a probabilistic evaluation of the result. The purpose of this paper is to extend a previous feasibility study to a finite element Timoshenko beam model of a full blade for which the model updating process is conducted through the novel approach with invertible neural networks (INNs). This type of artificial neural network is trained to represent an inversion of the physical model, which in general is complex and non-linear. During the updating process, the inverse model is evaluated based on the target model’s modal responses. It then returns the posterior prediction for the input parameters. In advance, a global sensitivity study will reduce the parameter space to a significant subset on which the updating process will focus. The finally trained INN excellently predicts the input parameters’ posterior distributions of the proposed generic updating problem. Moreover, intrinsic model ambiguities, such as material densities of two closely located laminates, are correctly captured. A robustness analysis with noisy response reveals a few sensitive parameters, though most can still be recovered with equal accuracy. And, finally, after the resimulation analysis with the updated model, the modal response perfectly matches the target values. Thus, we successfully confirmed that INNs offer an extraordinary capability for structural model updating of even more complex and larger models of wind turbine blades.

KW - model updating

KW - wind turbine

KW - rotor blade

KW - neural network

KW - digital twin

KW - Inverse modeling

UR - http://www.scopus.com/inward/record.url?scp=85127049924&partnerID=8YFLogxK

U2 - 10.5194/wes-2021-84

DO - 10.5194/wes-2021-84

M3 - Article

VL - 7

SP - 623

EP - 645

JO - Wind Energy Science

JF - Wind Energy Science

SN - 2366-7443

IS - 2

ER -