Main research topics in the Vibrations Department

© FBG / C. Bierwagen
© FBG / C. Bierwagen
© FBG / C. Bierwagen

The Vibrations Department is divided into the following main areas of research:

Structural Health Monitoring

In the field of SHM, research is being carried out on the combination,  application, and further development of vibration-based global SHM algorithms, which are applicable to measured data on rotor blades,  supporting structures of wind turbines, or civil engineering structures. Essential requirements for all methods are robustness and modularity, as well as considering uncertainties to make a probabilistic statement about the condition of the monitored structure possible.


Machine learning

The group was established to harness the power of cutting-edge machine learning and artificial intelligence techniques, both in their application and in advancing the underlying theory to achieve state-of-the-art performance. The group's research focuses on the application of data-driven methods across various domains, including structural health monitoring of engineering and composite structures. Key areas of interest include fault detection, predictive maintenance, time series prediction, data augmentation, sensor data fusion, and signal processing.


Acoustics

In acoustics, current research focuses on the investigation of atmospheric and topographic conditions on the sound propagation due to operating wind turbines. To this end, numerical forecast models as well as experimental concepts, based on extensive measurements, are being developed to validate both complex numerical models and analytical approaches.

 


Coupled Dynamic Systems

The development of efficient parameterized simulation models for complex structures under acceptable calculation times is a special challenge from a scientific point of view. In the working group main focus lies in the development of deterministic models and analyses for aero-elastic structures with different nonlinearities and interactions, which use energy-, linear and angular momentum-preserving time integration methods. Both the investigated and developed methods are being implemented into the inhouse-code DeSiO (Design and Simulation Framework for Offshore Support Structures), which is based on multi body systems and the Finite Element method. 


Model Update

The research group Model Update focuses on the localization and quantification of damage in structures using model-based structural health monitoring methods. In particular, the consideration of uncertainties in optimization methods is examined to achieve more accurate and reliable results, which aim to improve the reliability and safety, as well as to extend the lifespan, of buildings.


Recent research projects in the vibrations department

Structural Health Monitoring

  • German Research Facility for Wind Energy (DFWind)
    The project aims to lay the foundation of a research and development platform which concentrates on the usage of wind turbines throughout the entire functional chain in a so far unattained quality. The research is focused on the interaction of the subsystems as part of the overall structure, under consideration of mutual influences of two separate wind turbines and the effect on the integrated network as well. The ISD will be concentrating on intelligent measurement data analysis, Structural Health Monitoring as well as the calculation of coupled dynamical systems.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: Dr.-Ing. Tanja Grießmann, Stefan Wernitz, M.Sc., Benedikt Hofmeister, M.Eng.
    Year: 2016
    Funding: Federal Ministry for Economic Affairs and Energy - FKZ 0325936E
    Duration: 01.01.2016 – 31.12.2020
    © DLR
  • German Research Facility for Wind Energy (DFWind)
    The project aims to lay the foundation of a research and development platform which concentrates on the usage of wind turbines throughout the entire functional chain in a so far unattained quality. The research is focused on the interaction of the subsystems as part of the overall structure, under consideration of mutual influences of two separate wind turbines and the effect on the integrated network as well. The ISD will be concentrating on intelligent measurement data analysis, Structural Health Monitoring as well as the calculation of coupled dynamical systems.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: Dr.-Ing. Tanja Grießmann, M.Sc. Leon Liesecke, M.Sc Helge Jauken, M.Sc. Stefan Wernitz, M.Sc. Benedikt Hofmeister
    Year: 2020
    Funding: Federal Ministry for Economic Affairs and Energy
    Duration: 12/2020-12/2025
  • SMARTower – Monitoring of modular tower constructions and foundation behavior of wind turbine foundations
    The SMARTower research project focuses on enhancing the structural integrity and longevity of wind turbines through advanced monitoring techniques. Specifically, it aims to develop a data-driven structural health monitoring (SHM) system for the foundations and hybrid towers of wind turbines. The use of intelligent AI-supported monitoring systems is intended to make analysis more efficient through automation.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: Dr.-Ing. Tanja Grießmann, M.Sc. Niklas Römgens
    Year: 2022
    Funding: Federal Ministry for Economic Affairs and Climate Action (BMWK) over Projektträger Jülich (PTJ)
    Duration: 08/2022 - 07/2025
  • Reallabor – Innovationsbereich III – Teilprojekt III.1 (Reallabor IB3 TP III.1)
    Sub-project III.1 focuses on the development of a structural health monitoring concept for the continuous monitoring of the support structure, tower and rotor blades of offshore wind turbines. This is done by comprehensively analysing vibration and SCADA data. The project is also trialling population-based SHM approaches to develop minimum sensor concepts for the entire wind farm. The aim is to identify relevant operating states and system parameters as well as critical damage scenarios in order to extend the service life of a wind farm.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes, Dr.Ing. Tanja Grießmann
    Team: Dr.-Ing. Clemens Jonscher, M.Sc. Jonathan Thurn
    Year: 2024
    Funding: Niedersächsisches Ministerium für Wissenschaft und Kultur und VolkswagenStiftung
    Duration: 01.10.2024 – 30.09.2029
  • SysDamp – Innovative methods to determine damping for realistic service life predictions of new wind turbines
    The rotor blades of wind turbines are becoming increasingly longer and slimmer over time, which leads to significant deformations and vibrations. For realistic design and lifetime assessment, it is therefore essential to accurately determine and predict the damping of the overall system. The goal of the SysDamp project is to develop evaluation algorithms that can convert data measured in field tests into damping parameters. These parameters are then applied to future turbine designs.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: Dr.-Ing. Tanja Grießmann, M.Sc. Helge Jauken, M.Sc. Leon Minne, M.Sc. Leon Liesecke
    Year: 2024
    Funding: Federal Ministry for Economic Affairs and Climate Action (BMWK) over Projektträger Jülich (PTJ)
    Duration: 01/2024 - 12/2026
  • Vibration-based structural health monitoring via AI-driven transfer learning
    The objective of SP C02 is to combine the multivariate grey-box monitoring concept for a single structure (first funding period) with transfer learning methods from the second funding period to enable the transfer of knowledge and pre-existing measurement data from different structures. This will allow the implementation of a flexible and reliable SHM solution for several offshore megastructures.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: Dr.-Ing. Tanja Grießmann, Sören Möller
    Year: 2025
    Funding: German research foundation (DFG)
    Duration: 01.01.2025 – 31.12.2028

Acoustics

  • Development of an AI-based Geographic Information System for the Selection of Wind Energy Potential Sites within the Context of Species Conservation, Environmental Protection, and Climate Protection (WindGISKI)
    The aim of the project is to develop and evaluate an AI-based GIS system to identify suitable areas for wind turbines. The identification process will be automated and systematized through the use of modern technologies, with special consideration of immission, environmental, species and climate protection aspects. A significant improvement in both the quality and quantity of potential wind turbine sites is being aimed for. The focus of the ISD includes the combination of a sound simulation model with GIS data and the implementation of a shadow impact simulation.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: M.Sc. Tobias Bohne, M.Sc. Susanne Könecke
    Year: 2021
    Funding: Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection
    Duration: 01.12.2021 – 30.06.2025
  • Reallabor – Innovation area V – Subproject V.3 (Reallabor IB5 TP V.3)
    In subproject V.3, an acoustic model of an offshore wind farm will be developed, encompassing all relevant noise sources that may occur throughout its lifetime. The model is intended to be integrated with a hydrodynamic model of the North Sea to account for the influence of environmental conditions. In addition to the modeling work, extensive field measurements will be conducted in an offshore wind farm. These data will be used both to accurately specify the characteristics of the noise sources and to validate the model. The overarching goal is to enable precise, scenario-based assessments of underwater noise pollution and its ecological impacts.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes, Dr.-Ing. Tanja Grießmann
    Team: Tobias Bohne, M.Sc.; Nitesh Bhume, M.Sc.
    Year: 2025
    Funding: Niedersächsisches Ministerium für Wissenschaft und Kultur und VolkswagenStiftung

Coupled Dynamic Systems

  • Fully coupled mid-fidelity digital twin of a megastructure
    The central project Z01 of CRC 1463 is developing the Digital Twin of an offshore megastructure. This constitutes a real-time capable, adaptive virtual representation of the real structure, in which all relevant fluid-structure interactions are taken into account. In the current second funding phase, the focus lies on the extension of an in-house aero-hydro-servo-elastic simulation tool, which forms the core of the Digital Twin. Emphasis is placed on the more efficient and robust computation of both aero- and hydro-elastic coupled simulations.
    Led by: Prof. Dr.-Ing. habil. Raimund Rolfes
    Team: David Märtins, Daniel Schuster
    Year: 2025
    Funding: German research foundation (DFG)
    Duration: 01.01.2025 – 31.12.2028

Uncertainty

  • Well-founded meta-modeling of wind turbines as an example of stochastic, controlled structures (MetaWind)
    Smart systems, which mostly come from the field of communication technology and consist of a combination of networked, controllable, sensory and intelligent subsystems, have been omnipresent in media in recent years. Smart systems have actually been around for years in a wide variety of engineering fields. In civil engineering, an example are wind turbines with networked elements of measurement and control technology. For an economical and reliable design of these systems, their simulation is necessary. Due to the non-linear, stochastic, and controlled behaviour of such systems, the required computing time can be uneconomically high. In this case, substitute models, so-called meta-models, can be used to approximate the system behaviour. Although meta-models are already widely used today, their accuracy and efficiency are limited so far. Comprehensive analyses for complex systems are scarce. Therefore, MetaWind addresses this research gap and develops well-founded meta-models for wind turbines based on a comprehensive comparison of different variants.
    Led by: Dr.-Ing. Clemens Hübler
    Team: Franziska Müller, M.Sc.
    Year: 2019
    Funding: Leibniz Young Investigator Grant (LUH)
    Duration: 2019- 2021
    © ISD