Bayesian model updating of concrete-embedded miter gate anchorages and implications for design
Authors (first, second and last of 5)
Collection
Advanced Optimization Enabling Digital Twin Technology
A digital twin is a real-time digital/virtual representation of a physical product or process. The digital twin technology is centered around “individualized” digital models that capture the unique characteristics of individual product or process units. These models allow decision making to be optimized for each product or process unit, rather than based on the average characteristics of the entire population. This emerging technology poses new and challenging optimization problems at the forefront of model-based design, smart manufacturing, industrial IoT, machine learning (ML), and predictive maintenance. The industry-scale adoption of the digital twin concept entails creating novel optimization solutions that use data coming in from sensors and inspections (physical-to-digital) to provide decision makers with actionable information (digital-to-physical), thereby closing the digitalization loop. Major benefits include the ability to optimize control/maintenance actions to individual units and the potential to optimize the design of next-generation products.
This Special Issue is dedicated to the current state-of-the-art and future directions of advanced optimization enabling the digital twin technology. It will include original papers with clear relevance to the optimization of structures, fluids, or another major physics, contributed by researchers and practitioners from the fields of engineering design, smart manufacturing, structural health monitoring, prognostics and health management, model-based predictive control, and others.
Editors
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Chao Hu
Iowa State University, USA
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Vicente A. Gonzalez
University of Auckland, New Zealand
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Taejin Kim
JeonBuk National University, South Korea
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Omer San
Oklahoma State University, USA
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Zhen Hu
University of Michigan, USA
Articles (19 in this collection)
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The connection between digital-twin model and physical space for rotating blade: an atomic norm-based BTT undersampled signal reconstruction method
Authors (first, second and last of 7)
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A reinforcement learning hyper-heuristic in multi-objective optimization with application to structural damage identification
Authors (first, second and last of 4)
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A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
Authors (first, second and last of 10)
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A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Authors (first, second and last of 10)
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Health index construction with feature fusion optimization for predictive maintenance of physical systems
Authors (first, second and last of 5)
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Seismic fragility analysis of deteriorated bridge structures employing a UAV inspection-based updated digital twin
Authors (first, second and last of 6)
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Surrogate modeling of acoustic field-assisted particle patterning process with physics-informed encoder–decoder approach
Authors (first, second and last of 5)
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Digital Twin in smart manufacturing: remote control and virtual machining using VR and AR technologies
Authors (first, second and last of 5)
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Digital-twin-enhanced metal tube bending forming real-time prediction method based on multi-source-input MTL
Authors (first, second and last of 6)
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Effective band-selection algorithm for rolling element bearing diagnosis using AE sensor data under noisy conditions
Authors (first, second and last of 5)
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Diagnostics and prognostics of multi-mode failure scenarios in miter gates using multiple data sources and a dynamic Bayesian network
Authors (first, second and last of 5)
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Toward digital twin development for additively manufactured turbine blades with experimental and analytical methods
Authors (first, second and last of 4)
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A surrogate model to accelerate non-intrusive global–local simulations of cracked steel structures
Authors (first, second and last of 5)
