Abstract
Innovative production techniques, such as 3D printing of metals, require attention both in the production and in the post-production phase. In fact, such manufacturing processes introduce higher margins of uncertainty compared to more canonical processes. As a consequence, they require an increased effort to succeed in delivering representations for the so-called dynamic virtualization process. Virtualization encompasses the ensemble of activities that are aimed at formulating the virtual model of a given structure and subsequently validating and updating this model in order to guarantee a realistic and accurate response prediction in a broad range of operating conditions. This chapter explores the main challenges related to the mentioned limitations, in the context of a down-scaled industrially relevant case study: a 3D-printed scaled titanium Wind Turbine (WT) blade. The scaled blade has been the object of a complete virtualization process: from the design by means of conventional WT blade tests, up to its “Digital-Twin” establishment, where we exploit state-of-the-art Virtual Sensing (VS) techniques, due to their intrinsic capability of “enriching” the high-fidelity model’s predictions with information extracted from test data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Azam, S.E., Chatzi, E., Papadimitriou, C., Smyth, A.: Experimental validation of the Kalman-type filters for online and real-time state and input estimation. J. Vibration Control 23(15), 2494–2519 (2017)
Dertimanis, V.K., Chatzi, E.N., Azam, S.E., Papadimitriou, C.: Input-state-parameter estimation of structural systems from limited output information. Mech. Syst. Signal Process. 126, 711–746 (2019)
Papadimitriou, C., Fritzen, C.P., Kraemer, P., Ntotsios, E.: Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering. Struct. Control Health Monit. 18(5), 554–573 (2011)
Cumbo, R., Tamarozzi, T., Janssens, K., Desmet, W.: Kalman-based load identification and full-field estimation analysis on industrial test case. Mech. Syst. Signal Process. 117, 771–785 (2019)
Vettori, S., DiLorenzo, E., Peeters, B., Chatzi, E.: Virtual sensing for wind turbine blade full field response estimation in operational modal analysis. In: Proceedings of IMAC2020 International Conference (2020)
Avitabile, P., Pingle, P.: Prediction of full field dynamic strain from limited sets of measured data. Shock Vibration 19(5), 765–785 (2012)
Lourens, E., Reynders, E., De Roeck, G., Degrande, G., Lombaert, G.: An augmented Kalman filter for force identification in structural dynamics. Mech. Syst. Signal Process. 27, 446–460 (2012)
Azam, S.E., Chatzi, E., Papadimitriou, C.: A dual Kalman filter approach for state estimation via output-only acceleration measurements. Mech. Syst. Signal Process. 60, 866–886 (2015)
Maes, K.: Filtering techniques for force identification and response estimation in structural dynamics. PhD thesis, KU Leuven, 2016
Craig, R.J.: A review of time-domain and frequency-domain component mode synthesis methods. Int. J. Anal. Exp. Modal Anal. 2(2), 59–72 (1987)
Vettori, S., Di Lorenzo, E., Peeters, B., Chatzi, E.: A virtual sensing approach to operational modal analysis of wind turbine blades. In: Proceedings of ISMA2020 International Conference on Noise and Vibration Engineering, Leuven, Belgium (2020)
Vettori, S., Lorenzo, E.D., Cumbo, R., Musella, U., Tamarozzi, T., Peeters, B., Chatzi, E.: Kalman-based virtual sensing for improvement of service response replication in environmental tests. In: Model Validation and Uncertainty Quantification, vol. 3, pp. 93–106. Springer (2020)
Acknowledgements
The authors gratefully acknowledge the European Commission for its support of the Marie Sklodowska Curie program through the ITN DyVirt project (GA 764547).
The authors would like to also acknowledge DTU Wind Energy and the project “RELIABLADE: Improving Blade Reliability through Application of Digital Twins over Entire Life Cycle,” supported by the Danish Energy Agency through the Energy Technology Development and Demonstration Program (EUDP), Grant No. 64018-0068, the support of which is greatly appreciated.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Brzhezinski, H., Vettori, S., Di Lorenzo, E., Peeters, B., Chatzi, E., Cosco, F. (2023). On the Dynamic Virtualization of a 3D-Printed Scaled Wind Turbine Blade. In: Mao, Z. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-04090-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-04090-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04089-4
Online ISBN: 978-3-031-04090-0
eBook Packages: EngineeringEngineering (R0)