Digital Twins

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Digital Twins (Jumeaux Numériques)

Auteurs

  • Hani Pamella
  • Picaud Nicolas

Résumé (en français)

Les jumeaux numériques (DT) sont des représentations virtuelles de systèmes physiques, synchronisées avec des entités réelles via l’IoT, le Big Data et l’IA. Émergeant du programme Apollo et formalisés par la NASA, les jumeaux numériques sont devenus essentiels dans l’Industrie 4.0. Ces systèmes permettent la surveillance en temps réel, la maintenance prédictive et l’optimisation en créant des modèles dynamiques reliés à des objets physiques par des capteurs. Adoptés dans des domaines tels que la fabrication, la santé, les villes intelligentes et les transports, les jumeaux numériques réduisent les coûts, augmentent l’efficacité et soutiennent la durabilité. La croissance mondiale du marché des jumeaux numériques souligne leur potentiel à relever des défis tels que le changement climatique et la gestion des ressources.

Mots-Clé : Jumeau numérique, IoT, maintenance prédictive, simulation, modèle virtuel, système physique, données en temps réel, Industrie 4.0, optimisation, durabilité, intelligence artificielle, Big Data, villes intelligentes, santé, transports, efficacité énergétique, gestion des ressources, analyse prédictive.

Abstract (en anglais)

Digital Twins (DTs) are virtual representations of physical systems that synchronize with real-world entities through IoT, Big Data, and AI. Rooted in the Apollo program and formalized by NASA, Digital Twins have become pivotal in Industry 4.0. These systems enable real-time monitoring, predictive maintenance, and optimization by creating dynamic models linked to physical objects via sensors and connectivity frameworks. Widely adopted in manufacturing, healthcare, smart cities, and transportation, DTs reduce costs, enhance efficiency, and support sustainability. As the global Digital Twin market grows, its potential to address challenges such as climate change and resource scarcity highlights its transformative power across industries.

Keywords : Digital Twin, IoT, predictive maintenance, simulation, virtual model, physical system, real-time data, Industry 4.0, optimization, sustainability, artificial intelligence, Big Data, smart cities, healthcare, transportation, energy efficiency, resource management, predictive analytics.

Synthesis

Product lifecycle management has existed for decades; however, in 2002, a novel concept emerged to bridge physical and virtual systems, marking the initial development of a Digital Twin (DT)-like model [1]. The term Digital Twin was later formalized by John Vickers and gained clarity when NASA introduced a roadmap explicitly defining the concept [2]. The origins of digital twins can be traced back to the 1960’s, where scientists needed a living model of the Apollo mission in order to evaluate the failure that led to Apollo 13’s oxygen tank explosion [6]. This early exploration laid the groundwork for replicating physical systems virtually. Over time, this concept evolved into a cornerstone of Industry 4.0, which emphasizes the integration of smart and interconnected systems.

According to the ISO/IEC 20924 standard, a Digital Twin is a digital representation of a target entity with data connections that enable convergence between the physical and digital states at an appropriate rate of synchronization [3].

This idea of linking the physical and virtual systems saw a net growth in scientific interest from 2016 to nowadays as explained in the review [4].

A Digital Twin is a dynamic and adaptive virtual model that emulates the behavior of a physical system by continuously receiving real-time data to update itself throughout its lifecycle. By replicating the physical system in a digital environment, the Digital Twin enables organizations to predict potential failures, identify opportunities for improvement, and prescribe real-time actions to optimize performance or mitigate unexpected events.

Interest and Market

Why focus on digital twins? They enhance performance, cut costs and risks, boost product quality, and shorten time to market. These benefits have driven their adoption, particularly in Industry 4.0, the focus of this presentation. The global market for Industry 4.0 technologies is experiencing a significant growth, projected to reach USD 377.30 billion by 2029, from USD 130.90 billion in 2022 [7]. On the other hand, the Digital Twin market, a key component of Industry 4.0, is expanding fast. It is expected to grow from USD 17.73 billion in 2024 to USD 259.32 billion by 2032[5].

With the surge of IoT, Big Data and AI, coupled with an ever-growing demand for predictive analysis, digital twins are becoming more indispensable to businesses across a diverse range of industries.Whether optimizing mechanical systems in manufacturing or revolutionizing diagnostics and treatment in medicine, Digital Twins are transforming operations by providing actionable insights, enhancing efficiency, and enabling data-driven decision-making on an unprecedented scale. According to Gartner, 70% of large enterprises are expected to adopt Digital Twins by 2025, underscoring their growing significance [7]. Their ability to deliver real-time insights, predict system behaviors, and streamline operations has made them indispensable tools. For instance, Siemens uses Digital Twins for predictive maintenance, improving machine efficiency and reducing downtime[8].

Industry 4.0 and Digital Twins

The importance of Digital Twins is closely tied to the broader trends within Industry 4.0. The Industry 4.0 market size is projected to grow from USD 210.86 billion in 2024 to USD 605.22 billion by 2029, with a compound annual growth rate (CAGR) of 23.48% during the forecast period [9]. This growth underscores the expanding reliance on Digital Twins and other technologies. Hence the fact that businesses that use digital twins to achieve enhanced performance, cost savings, and accelerated time-to-market.

In industry, Digital Twins are used for predictive maintenance, optimizing production lines, and reducing unplanned downtime. In healthcare, they simulate organs and systems to aid in diagnostics and treatment planning, offering a revolutionary approach to personalized medicine. In smart cities, they optimize energy networks, reduce waste, and improve urban planning. In transportation, they enhance logistics and public transport systems by analyzing traffic patterns and resource allocation. For example, urban planners use Digital Twins to simulate and manage traffic flow, reducing congestion and enhancing public transport systems, showcasing the technology’s versatility and potential for societal impact.

Real world application

An example of a working digital twin can be found inside Rolls Royce aircraft engines. Rolls-Royce's "Blue Thread" is a digital platform central to their innovative use of digital twins for aerospace and other industries. It enables seamless data integration across the entire lifecycle of an engine, from design and manufacturing to in-service operation and maintenance. By leveraging the "Blue Thread," Rolls-Royce can simulate real-world engine performance, predict maintenance needs, and optimize operations using real-time data. This approach enhances reliability, efficiency, and sustainability by reducing downtime and enabling proactive service interventions. It also supports broader digital transformation goals, empowering predictive analytics and data-driven decision-making at scale.

During a flight, the Rolls-Royce engine equipped with sensors transmits real-time performance data via the "Blue Thread" platform. If an anomaly, such as an unusual temperature spike in a turbine, is detected, the digital twin replicates the engine's condition to analyze the issue. Predictive algorithms determine whether it is a minor fluctuation or a potential failure. If necessary, ground teams are alerted to prepare specific parts and technicians for maintenance upon landing, minimizing downtime and ensuring passenger safety without disrupting the flight schedule[10].

Technical aspects

At their core, Digital Twins consist of four key components: the physical object, IoT sensors, a digital model, and a connectivity framework. The physical object is the real-world entity being modeled, while IoT sensors capture real-time data to reflect its current state [11]. This data feeds into the digital model, which serves as a virtual replica for analysis and simulation

The connectivity framework establishes a bidirectional data flow, enabling updates to the Digital Twin and allowing it to influence the physical system. This dynamic interaction sets Digital Twins apart from simpler systems like digital shadows, which only offer unidirectional communication. Platforms such as Azure Digital Twins and Azure IoT Hub (both Microsoft services) exemplify how cloud-based solutions enable seamless integration, real-time data exchange, and actionable insights, further enhancing the capabilities of Digital Twins [12].

The lifecycle of a Digital Twin is a dynamic, iterative process that synchronizes physical and digital systems to optimize performance and decision-making. Furthermore, it can be mentioned that the Digital Twin’s lifecycle is quite similar to a CI/CD pipeline , emphasizing iterative improvement and seamless integration. It begins with the design phase, where the digital model is created to replicate the physical system’s behavior. Once deployed, the twin integrates with IoT sensors and data pipelines, establishing a continuous feedback loop for real-time data collection and analysis. During operation, the twin adapts to changes by running simulations and evaluating scenarios to predict failures, prescribe optimizations, and enable proactive maintenance. As the physical system evolves, the twin refines its predictive capabilities, ensuring continuous improvement and alignment. In the decommissioning phase, the twin archives insights and operational data to support future designs and innovation [13]. This continuous feedback process, akin to a CI/CD pipeline, drives ongoing refinement and synchronization, making Digital Twins indispensable for operational efficiency and long-term success.

Challenges and Opportunities

Despite its potential, implementing Digital Twins comes with challenges. Developers must efficiently manage large volumes of real-time data, ensure robust data privacy and cybersecurity measures, and maintain fault tolerance in critical systems. Additionally, integrating multiple technologies into a cohesive solution requires significant technical expertise and architectural planning.

As Digital Twin technology matures, its alignment with the goals of Industry 4.0 continues to open new opportunities. By optimizing energy use, reducing waste, and promoting renewable energy adoption, Digital Twins contribute to sustainability efforts. In healthcare, patient-specific Digital Twins enable personalized treatments, improving outcomes and efficiency. In product design, rapid prototyping and iterative simulations accelerate innovation, allowing businesses to respond to market demands more effectively.

Digital Twins also hold promise in addressing global challenges such as climate change and resource scarcity. By improving resource efficiency and fostering innovation in renewable energy systems, Digital Twins can play a pivotal role in creating sustainable solutions. Furthermore, their ability to provide detailed insights and real-time feedback makes them invaluable for disaster management, infrastructure resilience, and urban development.

The transformative power of Digital Twins lies in their ability to bridge the physical and digital worlds seamlessly. By integrating real-time data, advanced analytics, and predictive capabilities, they empower industries to operate more efficiently, innovate rapidly, and address complex challenges. As Digital Twins continue to evolve, they are poised to shape the future of industries and societies, making them a critical component of technological advancement and sustainability in the modern era. This growing reliance on Digital Twins signals their potential to revolutionize operations and drive progress across multiple domains, solidifying their role as a cornerstone of Industry 4.0 and beyond.

Références

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