Guest Editorial: Data-Driven Mechanics and Digital Twins for Ocean Engineering

Abstract

This special issue focuses on the topic of Data-Driven Mechanics and Digital Twins for Ocean Engineering. Two categories of papers are included in this issue; they deal with: (i) reduced-order modeling and data analytics; and (ii) data-driven computing and digital twins. In the first category, Yin et al. present the modal analysis of hydrodynamic forces in flow-induced vibrations using dynamic mode decomposition (DMD). Using snapshots of the flow field, spatio-temporal 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 data-driven analysis of the flow around a flexibly mounted cylinder via proper orthogonal decomposition (POD) analysis. The POD-based 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 three-dimensional wake flow structures. Such a body of work is useful for building reduced-order (surrogate) models that can be considered for multi-query 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 large-scale marine and offshore structures (e.g., offshore wind turbines, marine risers and pipelines). Moreover, projection-based 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 reduced-order modeling.