Abstract
Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
Highlights
With the recent wave of digitalization, the latest trend in every industry is to build systems and approaches that will help it during the conceptualization, prototyping, testing and design optimization phase and during the operation phase with the ultimate aim to use them throughout the whole product life cycle and perhaps much beyond
Based on our literature survey we present the following definition of digital twin: A digital twin is defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making
There are three most important ways in which this sector can make positive contributions (a) by making asset dataset available for research and validated model building (b) by actively participating in research through practical knowledge sharing (c) by being proactive in ‘projecting’ the insights obtained from predictive twins into their business applications to validate the usefulness of digital twins
Summary
With the recent wave of digitalization, the latest trend in every industry is to build systems and approaches that will help it during the conceptualization, prototyping, testing and design optimization phase and during the operation phase with the ultimate aim to use them throughout the whole product life cycle and perhaps much beyond. While the need for online monitoring, flexibility in operation, better inventory management and personalization of services are the most obvious market pull, availability of cheap sensors and communication technologies, phenomenal success of Machine Learning (ML) and Artificial Intelligence (AI), in particular, Deep Learning (DL), new developments in the computational hardware (Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU)), cloud and edge computing are certainly the major technology push In this regard, it will not be an overstatement to say that the digital twin concept is going to bring revolution across several industry sectors. The paper concludes with reflection and recommendations targeted towards five distinct stakeholders
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