Jonathan Moran Arellano PhD

Surrogate modelling for structural lifecycle assessment of offshore wind farms

Within the framework of a global lifecycle optimization of offshore wind turbine (OWT) installations, design and maintenance aspects should be adequately treated to support a reliable system functionality within a committed budget. The system-level analysis of OWT substructures, for instance, typically involves the complex interaction of its components raising to a large exploration of out-coming Quantities of Interest (QoI). Moreover, the probabilistic quantification of degradation mechanisms and structural failure events is prohibited in many scenarios, since an accurate collection of QoIs that typically follow a natural nonlinear behaviour relies on time-consuming high-fidelity simulations.

This PhD research proposes a surrogate-based modelling approach to assist design and maintenance aspects required of high-dimensional OWT structural reliability analyses by efficiently executing the least number of high-fidelity engineering simulations. Profiting from built-in features, such as informed-variance prediction, surrogate models can be actively trained by resampling the input design space in regions associated with a balance between the exploitation of small uncertainty zones nearby the limit state and exploration of large uncertainty zones far from the limit state. Furthermore, adaptive learning might be performed over a reduced latent design space that retains a feature extraction of high-dimensional reliability problems, overcoming the curse of dimensionality. Even when the emphasis of the proposed research is on structural lifecycle optimization, the surrogate-based modelling approach will be applicable to many other multi-physics engineering probabilistic designs to profit savings on computational demands, such as sensitivity analyses, global optimization, and decision-making.

Publications

• Morán A. Jonathan, Morato Pablo G., Rigo P., “Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models”. 14th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP14. Trinity College, Dublin, Ireland. [Accepted conference paper – submission and presentation to the conference by July 2023].
• Morán A. Jonathan, Morato Pablo G., Rigo P., “Budget-constrained life-cycle assessment of wind structural systems via actively trained surrogate models”. Wind Energy Science Conference 2023. University of Strathclyde, Glasgow, UK. [Accepted conference presentation by May 2023]. 
• Morán A. Jonathan, Morato Pablo G., Rigo P., “Budget constrained modelling for the reliability assessment of offshore wind substructures under accidental impact events” 18th EAWE PhD Seminar on Wind Energy. Bruges, Belgium. [Accepted conference paper – submission and presentation to the conference by November 2022].
• Salazar Priscilla, Morato Pablo G, Morán A. Jonathan, Rigo P., “Life-cycle management of offshore wind deteriorating structures under ship collision accidental events”. International Association for Life-Cycle Civil Engineering (IALCCE 2023). Politecnico di Milano, Milan, Italy. [Accepted conference paper– submission to the conference by July 2023].