Floating Modular Energy Islands (FMEIs) are modularized, interconnected floating structures that function together to produce, store, possibly convert and transport renewable energy. Recent technological advancements in the offshore energy sector indicate that the concept of floating offshore energy islands has the potential to become more cost-effective and more widespread than previously anticipated. This review is specifically meant as a basis for the development of new approaches to the sustainable exploitation of multi-energy sources in the offshore environment leveraging the know-how of existing technologies and, at the same time, exploring new solutions for the specific challenges of FMEIs. The paper critically analyzes the current state of data-driven approaches and structural health monitoring techniques in the offshore energy sector. It also covers topics such as met-ocean data, load estimation, platform dynamics, coupling actions, nonlinear dynamics of mooring lines, modelling considerations, and control of electrical subsystems. It is believed that this systematic and multidisciplinary review will facilitate synergies and further enhance research and development of offshore renewable energies.@en

We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection by adopting a generalized Maxwell model expressed by a Prony series, and deploy EUCLID for identification. The methodology is based on four ingredients: i. full-field displacement and net force data; ii. a very wide material model library — in our case, a very large number of terms in the Prony series; iii. the linear momentum balance constraint; iv. the sparsity constraint. The devised strategy comprises two stages. Stage 1 relies on sparse regression; it enforces momentum balance on the data and exploits sparsity-promoting regularization to drastically reduce the number of terms in the Prony series and identify the material parameters. Stage 2 relies on k-means clustering; starting from the reduced set of terms from stage 1, it further reduces their number by grouping together Maxwell elements with very close relaxation times and summing the corresponding moduli. Automated procedures are proposed for the choice of the regularization parameter in stage 1 and of the number of clusters in stage 2. The overall strategy is demonstrated on artificial numerical data, both without and with the addition of noise, and shown to efficiently and accurately identify a linear viscoelastic model with five relaxation times across four orders of magnitude, out of a library with several hundreds of terms spanning relaxation times across seven orders of magnitude.

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