Special Section on Digital Twins: A New Frontier in Critical Infrastructure Protection and ResilienceSource: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 010 ):;issue: 001::page 10301-1Author:Attoh-Okine, Nii
DOI: 10.1115/1.4064544Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: A digital twin (DT) is a computational model (or set of coupled) that evolves over time to persistently represent the critical structure, its components, system, or process. Digital twin underpins intelligent automation by supporting data-driven decision-making and enabling asset-specific analysis and system behavior. Within the context of critical infrastructure systems, the digital twins represent the flow of information among connected platforms. In the future, as many agencies turn to digital twin capabilities, they have to migrate toward continuous real-time performance models and calibrate by pairing data from real-time sensors, meters, weather, and other data. The digital twin can be used to run “what-if” scenarios, predict and prevent failures, provide early alerts of anomalies, and conduct predictive analysis. The strength of a digital twin is the interconnectivity of data and models. The main characteristics of a digital twin are:
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contributor author | Attoh-Okine, Nii | |
date accessioned | 2024-12-24T19:17:54Z | |
date available | 2024-12-24T19:17:54Z | |
date copyright | 2/1/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2332-9017 | |
identifier other | risk_010_01_010301.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4303684 | |
description abstract | A digital twin (DT) is a computational model (or set of coupled) that evolves over time to persistently represent the critical structure, its components, system, or process. Digital twin underpins intelligent automation by supporting data-driven decision-making and enabling asset-specific analysis and system behavior. Within the context of critical infrastructure systems, the digital twins represent the flow of information among connected platforms. In the future, as many agencies turn to digital twin capabilities, they have to migrate toward continuous real-time performance models and calibrate by pairing data from real-time sensors, meters, weather, and other data. The digital twin can be used to run “what-if” scenarios, predict and prevent failures, provide early alerts of anomalies, and conduct predictive analysis. The strength of a digital twin is the interconnectivity of data and models. The main characteristics of a digital twin are: | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Special Section on Digital Twins: A New Frontier in Critical Infrastructure Protection and Resilience | |
type | Journal Paper | |
journal volume | 10 | |
journal issue | 1 | |
journal title | ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg | |
identifier doi | 10.1115/1.4064544 | |
journal fristpage | 10301-1 | |
journal lastpage | 10301-1 | |
page | 1 | |
tree | ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 010 ):;issue: 001 | |
contenttype | Fulltext |