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"Surrogate infill criteria for operational fatigue reliability analysis" presented at IALCCE2018 by Rui Teixeira

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Analysis of Offshore Wind Turbine (OWT) fatigue damage is an intense, resource demanding task. While the current methodologies to design OWT to fatigue are quite limited in the way and amount of uncertainty they can account for, they still represent a relevant share of the total effort needed in the OWT design process. The robustness achieved in the design process is usually limited. To enable OWT to be more robust, an innovative methodology that tackles current limitations using a balanced amount of designing effort was developed. It consists of generating a short-term fatigue damage (DSH ) using a Kriging surrogate model that accurately accounts for uncertainty using an adaptive approach. The current paper discusses the application of a reinterpolation convergence to build a Kriging surrogate model that replicates DSH in OWT tower components. Different variables involved in the convergence are discussed. The discussion extends then to how the design could be improved by using different convergence scenarios for the Kriging surface. Cross-validation is used to train and validate the surrogate surface. The main goal is to give the designer a rationale on the trade-off between computational time and accuracy using the mentioned approach to design robust OWT towers. Results show that on a design basis two levels of approach may be efficient. In the first, if a very high computational cost is expected, a trade-off between accuracy and computational time must be considered and then, if the intention is to check how robust the current design is, a full convergence of the surface should be pursued.

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"Surrogate infill criteria for operational fatigue reliability analysis" presented at IALCCE2018 by Rui Teixeira

  1. 1. Reducing Uncertainty in Structural Safety Special Session SS6 Ghent, Belgium 28-31 October 2018
  2. 2. Outline Introduction Offshore wind Turbine systems Stress-cycle (SN) fatigue assessment Meta-modelling of SN fatigue Search function Results and application on a reliability analysis framework
  3. 3. Introduction Levelized Cost of Energy (LCOE) for different sources of renewable energy divided by region. Source : IRENA 2017 report. The demand for renewable energy is unquestionable. Innovation in the practices to design and operate Offshore Wind Turbines has been the main key driver to enhance competiveness (IRENA, 2017). Research along with regulatory framework are expected to be the main enablers of OWT development up to 2050. Scale-up, where the increase of the tower component height has been a major driver of competiveness for OWTs. Ref: IRENA. Renewable power generation costs in 2017. Technical report, International Renewable Energy Agency, 2017.
  4. 4. Offshore wind turbine systems
  5. 5. OWT fatigue design IEC61400 and DNV guidelines. Run multiple time domain simulations at operational states and count stresses and cycles using counting algorithm (e.g. rainflow counting) Plus SN curve and:
  6. 6. Meta-modelling of SN fatigue Gaussian process regression models, or Kriging models, have seen an increase in its application to structural problems. Polynomial Component : is Gaussian process with mean 0 and covariance . . Define a surrogate of short- term SN damage:
  7. 7. LHS DoE Approximation Common approach in literature works. Not consistent.
  8. 8. Learning criteria Kriging enables notion of improvement. Relation to the physical problem of fatigue. Learning criteria.
  9. 9. Comparison with standard methodology Robust even when only the corner of the space were given. Convergence to the 1 year prediction.
  10. 10. Comparison with standard methodology Compare with the traditional binning of data. Reduction of computational time up to 80% without compromising accuracy. Reduction never inferior to 50% for all the cases studied. SN slopes of 3, 5 and double 3 and 5.
  11. 11. Application for reliability analysis Allows to define multiple design surfaces. Each one replicates the IEC design assessment. Distribution of design SN fatigue based on the uncertainty of the design reliability considerations. Use the noise component:
  12. 12. Conclusions A meta-modelling technique was successfully implemented in order to reduce the computational time of the fatigue design for OWT analysis. It uses a Gaussian process predictor to surrogate the stress-cycle fatigue damage from different operational states. It was applied to the tower component, but the same methodology can be extended to any other component. The meta-model probabilistic behaviour was of interest to define a In a reliability analysis framework, the main interest is of the surrogate approach presented is the reduction of computational time that may enable reliability based optimization procedures, which challenging to apply for SN fatigue analysis due to their cost.
  13. 13. The TRUSS ITN project (http://trussitn.eu) has received funding from the European Horizon 2020 research and innovation programme under the Marie -Curie grant agreement No. 642453

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