Digital Twin: Values, Challenges and Enablers

1 Digital Twin: Values, Challenges and Enablers Adil Rasheed1;4, Omer San2 and Trond Kvamsdal3;4 Abstract—A digital twin can be defined as an adaptive model enable convergence between the physical and virtual states. of a complex physical system. Recent advances in computational According to the authors in [5], digital twins represent real pipelines, multiphysics solvers, artificial intelligence, big data objects or subjects with their data, functions, and communi- cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to cation capabilities in the digital world. reality. Digital twinning is now an important and emerging Although the first mention of the word digital twin can be trend in many applications. Also referred to as a computational traced back to only the year 2000 when Grieves mentioned it in megamodel, device shadow, mirrored system, avatar or a syn- the context of manufacturing, without a formal mention, sev- chronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and eral industries and organizations had been exploiting the idea operate cyber-physical intelligent systems, but also in how we at varying levels of sophistication. Some examples of these advance the modularity of multi-disciplinary systems to tackle are the log of patients health information and history tracking, fundamental barriers not addressed by the current, evolutionary online operation monitoring of process plants, traffic and logis- modeling practices. In this work, we review the recent status tics management, dynamic data assimilation enabled weather of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of forecasting, real-time monitoring systems to detect leakages in the current challenges and enabling technologies along with oil and water pipelines, and remote control and maintenance recommendations and reflections for various stakeholders. of satellites or space-stations. More recently, the necessity Index Terms—Digital Twin, Artificial Intelligence, Machine to formalize and utilize the full potential of digital twin Learning, Big Data Cybernetics, Hybrid Analysis and Modeling concepts arises from a combination of technology push and market pull. While the need for online monitoring, flexibility in operation, better inventory management and personalization of services are the most obvious market pull, availability of I. INTRODUCTION cheap sensors and communication technologies, phenomenal With the recent wave of digitalization, the latest trend in success of Machine Learning (ML) and Artificial Intelligence every industry is to build systems and approaches that will help (AI), in particular, Deep Learning (DL), new developments in it not only during the conceptualization, prototyping, testing the computational hardware (Graphics Processing Unit (GPU) and design optimization phase but also during the operation and Tensor Processing Unit (TPU)), cloud and edge computing phase with the ultimate aim to use them throughout the whole are certainly the major technology push. In this regard, it will product life cycle and perhaps much beyond. While in the first not be an overstatement to say that the digital twin concept phase, the importance of numerical simulation tools and lab- is going to bring revolution across several industry sectors. In scale experiments is not deniable, in the operational phase, the this context, the current paper is a small effort to present a potential of real-time availability of data is opening up new reasonably comprehensive overview of the concept. In doing avenues for monitoring and improving operations throughout so we plan to answer the following questions: the life cycle of a product. Grieves [1] in his whitepaper named the presence of this virtual representation as ”Digital Twin”. • What is a digital twin? Since its presence among the most promising technology • Is digital twin a new concept or has it existed with different names in the past? arXiv:1910.01719v1 [eess.SP] 3 Oct 2019 trends in Gartner’s recent report [2], the Digital Twin concept has become more popular in both academia and industry (e.g., • What values are expected out of the digital twin concepts? see [3] for a non-exhaustive glimpse at the major patented • What are the major challenges in building digital twins? developments), apparently with many different attributes to • What are the existing and emerging enabling technologies include or exclude in its definition. For example, Hicks [4] to facilitate its adoption? differentiates a digital twin from a virtual prototype and • What will be the social implications of this technology? redefines the concept as an appropriately synchronized body • What is expected from relevant stakeholders to extract of useful information (structure, function, and behaviour) of a maximum benefits from the digital twin technology? physical entity in virtual space, with flows of information that In the current paper we view digital twin of a physical asset as the schematic shown in Fig. 1. Any physical asset generates 1 Department of Engineering Cybernetics, Norwegian University of Science data which requires real-time processing (shown by green and Technology, Trondheim, Norway (e-mail: [email protected]) 2Mechanical and Aerospace Engineering, Oklahoma State University, Still- colored arrows and boxes) for mainly two purposes; informed water, Oklahoma, 74078-5016 USA (e-mail: [email protected]) decision making and for real-time optimization and control of 3 Department of Mathematical Sciences, Norwegian University of Science the physical asset. Additionally, the recorded data can be per- and Technology, Trondheim, Norway (e-mail: [email protected]) 4Department of Mathematics and Cybernetics, SINTEF Digital, Trondheim, turbed to do offline ”what-if ?” analysis, risk assessment and Norway uncertainty quantification (shown in grey). In the latter context 2 Informed decision making Real time control and optimization Physical Asset Real-time observation Better insight Hidden information Bigdata (4V) Data pre- in data (Volume, Velocity, processing Variety, Veracity) Interpretable Potential contingencies residue Hybrid Models Secured communicationtechnologies Uninterpretable Interpretable residual Data-driven models Noise Uninterpretable Hypothetical Data-driven models Updated hybrid Situation model Digital Twin Risk Assessment What if ? analysis Process optimization Uncertainty Quantification Digital Siblings Fig. 1: A generalized Digital Twin framework: Real-time data from an onshore wind farm is processed in real-time to infer the quantity of interest. This quantity can either be presented to decision makers or can be used for optimal control of the wind farm. The real-time data can also be perturbed to generate hypothetical scenarios that can be analyzed for “what if”, risk analysis or evaluating the impact of mitigation strategies. it is more appropriate to term the various virtual realizations of 1) Real-time remote monitoring and control: Generally, it the physical asset as ”Digital Siblings”. With Fig. 1 in mind we is almost impossible to gain an in-depth view of a start this paper with a brief description of eight values (Section very large system physically in real-time. A digital twin II) that any digital twin is capable of generating followed owing to its very nature can be accessible anywhere. The by a presentation of the state-of-the-art technology in five performance of the system can not only be monitored but diverse application areas (Section III). Common challenges also controlled remotely using feedback mechanisms. across these application areas are identified in Section IV. 2) Greater efficiency and safety: It is envisioned that digital To address these challenges, certain enabling technologies are twinning will enable greater autonomy with humans required. Some of these technologies already exist but most of in the loop as and when required. This will ensure them are at different stages of development. A comprehensive that the dangerous, dull and dirty jobs are allocated overview of the enabling technologies is presented in Section to robots with humans controlling them remotely. This V followed by a section on the socio-economic impacts of way humans will be able to focus on more creative and the technology (Section VI). Finally, the paper concludes with innovative jobs. reflection and recommendations targeted towards five distinct 3) Predictive maintenance and scheduling: A comprehen- stakeholders. sive digital twinning will ensure that multiple sensors monitoring the physical assets will be generating big II. VALUE OF DIGITAL TWIN data in real-time. Through a smart analysis of data, faults Building up on a report from Oracle [6], the following eight in the system can be detected much in advance. This will value additions of digital twin are identified: 3 enable better scheduling of maintenance. key aspects of human physiology crucial for the understanding 4) Scenario and risk assessment: A digital twin or to be of drug effects. By combining several such OOC one can more precise a digital siblings of the system will enable generate a full body-on-a-chip (BOC). Skardal et al. [12] what-if analyses resulting in better risk assessment. It gives the progress made in this direction. Although both OOC will be possible to perturb the system to synthesize un- and BOC are seen as disruptive technologies in the context

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