{"title":"Guest Editorial: Data-Driven Mechanics and Digital Twins for Ocean Engineering","authors":"R. Jaiman, L. Manuel","doi":"10.1115/1.4056012","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":50106,"journal":{"name":"Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4056012","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Offshore Mechanics and Arctic Engineering is an international resource for original peer-reviewed research that advances the state of knowledge on all aspects of analysis, design, and technology development in ocean, offshore, arctic, and related fields. Its main goals are to provide a forum for timely and in-depth exchanges of scientific and technical information among researchers and engineers. It emphasizes fundamental research and development studies as well as review articles that offer either retrospective perspectives on well-established topics or exposures to innovative or novel developments. Case histories are not encouraged. The journal also documents significant developments in related fields and major accomplishments of renowned scientists by programming themed issues to record such events.
Scope: Offshore Mechanics, Drilling Technology, Fixed and Floating Production Systems; Ocean Engineering, Hydrodynamics, and Ship Motions; Ocean Climate Statistics, Storms, Extremes, and Hurricanes; Structural Mechanics; Safety, Reliability, Risk Assessment, and Uncertainty Quantification; Riser Mechanics, Cable and Mooring Dynamics, Pipeline and Subsea Technology; Materials Engineering, Fatigue, Fracture, Welding Technology, Non-destructive Testing, Inspection Technologies, Corrosion Protection and Control; Fluid-structure Interaction, Computational Fluid Dynamics, Flow and Vortex-Induced Vibrations; Marine and Offshore Geotechnics, Soil Mechanics, Soil-pipeline Interaction; Ocean Renewable Energy; Ocean Space Utilization and Aquaculture Engineering; Petroleum Technology; Polar and Arctic Science and Technology, Ice Mechanics, Arctic Drilling and Exploration, Arctic Structures, Ice-structure and Ship Interaction, Permafrost Engineering, Arctic and Thermal Design.