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

verfasst von

Pablo Noever-Castelos, David Melcher, Claudio Balzani

Abstract

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.

Organisationseinheit(en)

Institut für Windenergiesysteme

Externe Organisation(en)

Fraunhofer-Institut für Windenergiesysteme (IWES)

Typ

Artikel

Journal

Wind Energy Science

Band

7

Seiten

623-645

Anzahl der Seiten

23

ISSN

2366-7443

Publikationsdatum

16.03.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Ingenieurwesen (insg.), Numerische Mechanik

Fachgebiet (basierend auf ÖFOS 2012)

Computational Engineering

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie, SDG 9 – Industrie, Innovation und Infrastruktur

Elektronische Version(en)

https://doi.org/10.5194/wes-2021-84 (Zugang: Offen)
https://doi.org/10.5194/wes-7-623-2022 (Zugang: Offen)