Please find attached the Call for Papers for the Special Issue on Digital Twins: A New Frontier in Critical Infrastructure Protection and Resilience.
Update: Submissions are open until January 31, 2023.
Guest Editors
- Nii Attoh-Okine, PhD, University of Delaware, USA, okine@udel.edu
- Yaw Adu-Gyamfi, PhD, University of Missouri, USA, adugyamfiy@missouri.edu
Aims & Scope
A digital twin 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 contexts 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 towards 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
a) trustworthy and accurate digital representation of the critical infrastructure systems which can provide a framework for computational speeds, hence model order reduction;
b) feedback loops that can enable near real-time data transfer between the virtual replicate and critical infrastructure systems;
c) numerical, including machine learning which provides a platform for the fusing of data and models, and transfers of learning processes that can provide prognostic capabilities.
The Special Issue is aimed at gathering contributions that discuss the formulation and solution of digital twin application in critical infrastructure systems. The papers will cover both theoretical and case studies addressing how the digital twin is tied to predictive maintenance and resilience.