Digitalization of Large-Scale Testing Facilities for the Wind Industry: DIGIT-BENCH Digital Twin

Author:

Baş Elif Ecem,Abbiati Giuseppe,Gonçalves Gomes Cláudio Ângelo,Jassmann Uwe,Larsen Peter Gorm

Abstract

Abstract Testing of large wind turbine components plays a central role in delivering reliable yet cost-effective technology. However, these experiments are often lengthy and costly. Fatigue testing of a wind turbine blade might take up to 12-14 months, whereas highly accelerated lifetime testing of a nacelle demands 6-8 months. The exchange of simulation models and data between OEMs and test facilities is recognized as a critical factor in the planning of an experimental campaign. In fact, OEMs are typically very protective of their industrial secrets, and sharing such sensitive information may constitute a threat. It follows that the use of simulation models to enable more effective experimentation is not pursued efficiently. Digital twins are emerging as a key enabling technology to improve the operation & maintenance of test benches for the wind industry. A digital twin combines physical systems and their digital models into a cyber-physical system to provide functionalities that cannot be attained by either physical or digital assets independently. The seamless integration of the test bench with digital models offered by a digital twin is expected to enhance the interaction between OEM and test bench operators. This proceeding illustrates the status of the development of the DIGIT-BENCH digital twin developed by R&D Test Systems (R&D) and Aarhus University to serve large-scale test facilities for the wind industry. The digital twin utilizes FMI-based co-simulation to enable the coupling of physical/digital components in an industrial-secret-friendly environment. The digital twin concept is demonstrated on a 2-degrees-of-freedom test bench installed at Aarhus University.

Publisher

IOP Publishing

Reference17 articles.

1. Virtual Model Development of the Load Application System of a Wind Turbine Nacelle Test Bench for Hybrid Test Applications;Siddiqui;Journal of Dynamic Systems, Measurement, and Control,2024

2. Digital Twin: Enabling Technologies, Challenges, and Open Research;Fuller;IEEE Access,2020

3. Hardware-in-the-Loop Wind Turbine System Test Benches and Their Usage for Controller Validation;Jassmann,2018

4. Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications;Wagg;ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg,2020

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