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WP2: Structural Health Monitoring, Progress Report

WP2: Structural Health Monitoring, Progress Report. 17 th and 18 th September 2019. Progress against tasks for each Work Package. 2.1 . Image analysis and methodologies for determining the mechanical integrity of wind turbine blades.

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WP2: Structural Health Monitoring, Progress Report

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  1. WP2: Structural Health Monitoring, Progress Report 17th and 18th September 2019

  2. Progress against tasks for each Work Package 2.1. Image analysis and methodologies for determining the mechanical integrity of wind turbine blades. PhD student continues to work on this project, having just passed his 9 month review. Jack was one of the Durham attendees at the 3-day ‘EPSRC Prosperity Partnership Introduction to Offshore Wind Energy’ training event held in Hull during May. Jack has completed a small comparative study on deep neural networks for fine-grain image classification ahead of access to the Orsted data (contract pending, with DU).

  3. 2.2. Structural health monitoring of blades using guided waves. Progress against tasks for each Work Package • Implemented a representative test case in the lab, measuring guided waves using a laser vibrometer and PZTs. • Discovered potential for complex modelling of guided waves in composite structures and work will continue to determine effect of damage on this behavior with the aim to develop damage detection methods. • Produced a conference paper for the International Conference on Structural Health Monitoring (IWSHM), Title: Health Monitoring Of Composite Structures By Combining Ultrasonic Wave Data.To be presented in Stanford, Sept 2019. • Presented his work at technical meeting with Siemens-Gamesa earlier this month. • Plans for the next 6 months + are to develop a robust Bayesian waves mapping method that will work with the active learning algorithms and look at specimens to be provided by SGRE. • Incorporating ‘physics-based’ models (in collaboration with T. Rogers and E.J. Cross) and looking into how novelty index can help to determine damage location, type and potentially severity. Testing on full scale Glider wing. • 3-day ‘EPSRC Prosperity Partnership Introduction to Offshore Wind Energy’ training event held in Hull during May.

  4. 2.3 Optimal sensors placement for blade Structural Health Monitoring. Progress against tasks for each Work Package • Progressing very well. Started February 2019. Initial few months spent taking courses on structural dynamics and condition monitoring an literature review. • Produced mind map poster for DRG poster event on vibration based optimal sensor placement. • Started working with some example experimental data on an aluminium plate with several complex geometries. There are numerous bolts on this plate, which can be removed to simulate damages.  In the second experiment, different data on the glider fibre glass wing will be collected, including vibration data and temperature. • In the next steps the accessible data can be compared with the data generated by finite element (FE) model to evaluate the similarity between the real structure and FE model.   Afterwards, the accelerometer and the strain gauge will be implemented on the plate.  The same damage states should be generated as before sequentially. These two types of sensors will be put on the same point but different sides of the plate to collect data. • Presented work at SGRE technical meeting. • 3-day ‘EPSRC Prosperity Partnership Introduction to Offshore Wind Energy’ training event held in Hull during May.

  5. A case study of an experiment on a glider wing The structure under investigation was a glider wing. The damage was simulated by adding mass blocks on the gilder wing. Two mass blocks were used, and the mass was 860g and 900g respectively. The wing was excited by a electrodynamic shaker. A white Gaussian excitation was generated. The FRFs were used for base measurements, which can be processed to acquire transmissibility if necessary. Temperature and humidity in the chamber were also recorded. Seven different temperatures were applied, which ranged from to Tip Trailing edge Leading edge Root The deployment of sensors on the glider wing

  6. 2.4. Improved modelling of wind turbine blade erosion on an individual turbine. Progress against tasks for each Work Package • PhD student Aidan Duffy started work on this project on 1st May 2019. • Aidan was one of the Durham attendees at the 3-day ‘EPSRC Prosperity Partnership Introduction to Offshore Wind Energy’ training event held in Hull during May. • Aidan has started to carry out CFD simulations of the flow around the DTU 10MW reference turbine blades as a first modelling exercise. • A project kick-off meeting involving Durham (Hogg, Ingram & Duffy), Ørsted (Kronborg & Veraat) & Siemens Gamesa (Loeven & Laustsen) took place in Aalborg Denmark on 30th August 2019. This also included a tour of the Aalborg Blade Manufacturing Facility. • Following the kick-off meeting it is planned to carry out a second set of high fidelity 2-D CFD calculations using modelling changes agreed at the project kick-off meeting and switching to the NACA 63618 blade section used on SG’s 58 turbine, which SG will supply coordinates for. The calculations will then move to 3-D simulations. • An M.Eng final year undergraduate student (Yuri Davy) will undertake a project starting October 2019 working with Aidan on some preliminary wind tunnel tests to generate benchmark test data on blade erosion effects for model validation purposes.

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  8. 2.5. Predicting critical failures in wind turbine blades by modelling populations of wind turbines. Progress against tasks for each Work Package • The project is about population-based SHM, with some grey-box modelling where appropriate. • Progressing very well. Started February 2019. Initial few months spent taking courses on structural dynamics and condition monitoring an literature review. • Produced mind map poster for DRG poster event on understanding the physics we might need to take into account when modelling a wind farm as a population. • Started working with some example SCADA data and mapping the changes across a farm; we are now working on a data-driven model of power across a farm; this will constitute a field for the population-based SHM approach and at the moment is a mapping of the wind environment across the farm (wake model). IMAC abstract to be submitted for this work • Presented work at SGRE technical meeting. • Next steps are to consider how to inform the data-driven map with knowledge of the fluid dynamics and the control action in each turbine. • 3-day ‘EPSRC Prosperity Partnership Introduction to Offshore Wind Energy’ training event held in Hull during May.

  9. Study the effect of spatial changes in EOVs – environmental mapping To build a data-driven model that predicts the spatial pattern in environmental conditions Coordinates of all turbines Wind speed from a fixed subset of turbines GP Example of the spatial pattern in environmental conditions (wind speed) across a wind farm and how it evolves with time (Lillgrund wind farm)

  10. PDRA 1. Predicting critical failures in wind turbine blades by modelling populations of wind turbines. Progress against tasks for each Work Package • The aims and objectives of the physics-based SHM approach are to create reduced order FE and multi-degree-of-freedom models to the following candidate materials. • Plans are to carry out fatigue loading to initiate and propagate cracks. Coupon tests are also planned to identify the material properties of the composite materials. In terms of modelling, the idea is to develop a validated FE model for the plates and incorporate damage modelling to it. • Wind turbine blades: B58-00 (58m) wind turbine bladeexperimental test January-May 2020. And physics based modelling. • The patent has been filed: 2018W17601 and 201824500. Filing date is 8.2.2019. • Two conference paper: IWSHM, 2019 and Wind Energy Science Conference 2019 (WESC 2019). • 3-day ‘EPSRC Prosperity Partnership Introduction to Offshore Wind Energy’ training event held in Hull during May. • Journal paper has been submitted (the patent had to filed first and now ready to be submitted): Kartik Chandrasekhar, Nikolaos Dervilis, Elizabeth J. Cross, NevenaStevanovic, Keith Worden: Damage detection in operational wind turbine blades using a new machine learning approach, 2019.

  11. Developed and compared different ROM techniques. For example, on a plate, the following is seen for the natural frequencies (in Hz)…

  12. All done  New and last PDRA on WP2, C Wick will work on population based monitoring (in collaboration with Gloria) and fleet performance as well very big data bases. Risks and Opportunities Very minor: 2.1. Image analysis and methodologies for determining the mechanical integrity of wind turbine blades. Durham. Contract pending, with Orsted and DU but soon be done and project kick-off meeting to be arranged.

  13. B58-00 (58m) wind turbine blade experimental test January-May 2020, funding secured and planning meeting regarding the test done in Aalborg/Brande 3rd and 4th September. • Progress meeting regaring DRG PhDs and RAs in Brande 3rd and 4th September. • Patent SG filed (DRG-Chandrasekhar, Dervilis, Cross, Worden, Stevanovic). • 6 Conference papers • 2 Journal paper accepted for publication. • 2 Journal papers submitted and under review. • 3 invited talks • Lizzy to got the Achenbach Medal which has been created to recognize an individual (within 10 years of PhD) who has made an outstanding contribution to the advancement of the field of Structural Health Monitoring. IWSHM, 2019. Outcomes and Outputs

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