Digital Twin & Asset Management

Developing methodologies to elevate the capability level of Digital Twins from 0-2 to 3-5 (autonomy).

Work Package 4 is lead by Adil Rasheed from NTNU and Kjetil Johannessen from SINTEF Digital. Its focus areas are Digital twin for wind energy, optimal farm control and asset management.

Anticipated results:

  • High level of physical realism in DTs
  • Max. power production with min. asset degradation
  • Probabilistic predictive maintenance

Hypothesis

Digital Twin with high level of realism will be a paradigm shift for informed decision making

Methodology and research tasks

Digital Twin adapted for wind energy

Framework development, optimised sensor placement, novel modelling paradigm.

Optimal Park Control

Holistic Modelling, Combined optimisation for energy production, asset degradation and its integration into the power grid, evaluation of farm control strategies.

Asset Management

Condition Monitoring (CM), Predictive Maintenance and improved strategies for life extension.

Informed public engagement

Augmented reality will be employed to inform the public of future impacts (WP5.3) of the upcoming wind farm.

Main results from 2021

Digital Twin adapted for wind energy

Conceptualising Digital Twins for wind energy:
  • A technology watch on the topic of digital twins was conducted to establish the state-of-the-art in the technology. 15 industry partners provided detailed feedback which has been processed and sent back to them for their approval for circulation and publication.
  • Work on a perspective article titled “A digital twin framework for wind energy” is ongoing.
Novel modelling paradigm:
  • A conference paper titled “Machine-learning based non-intrusive parametric reduced-order model for flows around aerofoils and wind turbine blade” was submitted to DeepWind.
  • Ongoing work on a conference paper titled “Hybrid analysis and modelling as a digital twin enabler for wind energy”.

Farm flow modelling

Multiscale wind modelling:
  • A multiscale wind prediction model has been set up for the Bessaker Wind Farm.
  • Work on a paper titled “Reinforcement learning for stabilising IEA 15MW offshore wind turbine under varying wind conditions” is underway.
  • Nord2000 noise models applied to Bessaker use case for idealised wind conditions.
  • Reduced Order Modelling of wakes using a hybrid approach.
  • A journal article titled “A hybrid POD approach for the solution of transient turbulent flow problems” is ready for submission.

Asset Management

A novel Corrective Source Term Approach (CoSTA) was developed as a HAM tool for structural health monitoring.
  • First draft of a journal article titled “Physics Guided Neural Network-assisted Corrective Source Term Approach to Hybrid Analysis and Modelling” is ready for submission.
  • Predictive maintenance and decision support for wind energy applications.
  • A conference paper titled “A review of fault prognostic models for predictive maintenance of offshore wind turbines” was submitted to Deep Wind.

Informed public engagement

Development of tools for visualising datasets:
  • A first version of the Augmented Reality tool was demonstrated.
Picture showing a room with an augmented reality overlay and two hands also overlayed.
Holo Lens application for augmented reality inspection of wind farms

Innovations