Special Section on DataDriven Mechanics and Digital Twins for Ocean Engineering

Abstract

This Special Section issue focuses on the topic of DataDriven Mechanics and Digital Twins for Ocean Engineering. Two categories of papers are included in this section that deals with (i) reducedorder modeling and data analytics and (ii) datadriven computing and digital twins. In the first category, Yin et al. presented the modal analysis of hydrodynamic forces in flowinduced vibrations using dynamic mode decomposition (DMD). Using snapshots of the flow field, spatiotemporal evolution characteristics of the wake patterns are analyzed. The dominant DMD modes with their corresponding frequencies are identified and used to reconstruct the flow fields. In another paper in this category, Janocha et al. presented a 3D large eddy simulation and datadriven analysis of the flow around a flexibly mounted cylinder via proper orthogonal decomposition (POD) analysis. The PODbased modal extractions are performed on slices in the wake to identify the coherent structure in the flow. Vortex shedding modes are analyzed and classified by examining threedimensional wake flow structures. Such a body of work is useful for building reducedorder (surrogate) models that can be considered for multiquery analysis, design optimization, and feedback control. However, these POD/DMD studies are restricted to linear physics as well as to idealized canonical geometries. There is a need for further extension to largescale marine and offshore structures (e.g., offshore wind turbines, marine risers, and pipelines). Moreover, projectionbased POD/DMD techniques generally face difficulties to scale for highly nonlinear turbulent flow. Nonlinear model reduction and deep neural networks (e.g., convolutional autoencoders) are possible alternatives to be explored for advanced reducedorder modeling.