[Paper Review] Digital Twin: Values, Challenges and Enablers

Adil Rasheed, Omer San+1 more|arXiv (Cornell University)|Oct 3, 2019

Digital Transformation in Industry474 references74 citations

TL;DR

A comprehensive overview of digital twin concepts, highlighting its values, application domains, common challenges, enabling technologies, and socio-economic implications.

ABSTRACT

A digital twin can be defined as an adaptive model of a complex physical system. 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. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

Motivation & Objective

• Summarize the concept of a digital twin and how it relates to virtual representations of physical assets.
• Identify the eight value additions that digital twins provide to industries and operations.
• Survey diverse application areas (health, meteorology, manufacturing, education, cities/transport/energy) to illustrate state-of-the-art and challenges.
• Outline common challenges in digital twin development across domains.
• Present enabling technologies in five categories to address these challenges.
• Discuss socio-economic impacts and stakeholder recommendations.

Proposed method

• Review and synthesize existing literature and industry usage to define digital twin concepts and terminology.
• Categorize values, applications, and challenges based on cross-domain analysis.
• Describe enabling technologies in five categories and relate them to challenges.
• Provide reflections and recommendations for stakeholders based on the synthesis.

Experimental results

Research questions

• RQ1 What is a digital twin and how does it relate to prior concepts and nomenclature?
• RQ2 What values and benefits do digital twins offer across industries?
• RQ3 What are the major challenges in creating and deploying digital twins?
• RQ4 What enabling technologies can address these challenges across domains?
• RQ5 What are the socio-economic implications and stakeholder recommendations for digital twin adoption?

Key findings

• Digital twins act as adaptive models enabling real-time monitoring, control, and what-if analysis via digital siblings.
• Eight value additions were identified, including real-time monitoring, efficiency and safety, predictive maintenance, risk assessment, collaboration, decision support, personalization, and better documentation.
• Applications span health, meteorology, manufacturing, education, and cities/transport/energy, each with domain-specific drivers and challenges.
• Common challenges include data security, data quality, latency, real-time simulation, data fusion, analytics, transparency, and cross-domain generalization.
• Enabling technologies are organized into physics-based modeling, data-driven modeling, big data cybernetics, infrastructure/platforms, and human-machine interfaces.
• The paper discusses socio-economic impacts and provides stakeholder-oriented recommendations.