The concept of digital twins has emerged as a powerful tool across various industries in recent years, revolutionizing the way organizations design, operate, and manage complex systems.
From astronomy to smart cities, digital twins are reshaping the landscape of innovation, and driving efficiency, productivity, and sustainability.
Data-driven learning systems
Definition
A digital twin is a virtual replica or simulation of a physical asset, process, or system that enables its real-time monitoring, analysis, and optimization.
The object under study is equipped with various sensors linked to vital areas of its functionality. These sensors generate data on several aspects of the physical object’s performance. The data are then sent to a processing system and applied to the digital copy.
Once populated with the data, the digital copy can be used to run simulations, investigate performance issues and develop possible improvements, all with the aim of generating valuable information.
The 4 types of digital twin
Digital twins are divided into 4 levels, bottom-up, depending on the integration level of data and parameters:
- Level 1: Component Twin
- This represent the smallest elements of a system such as a specific part of the equipment or product.
- Level 2: Digital Product Twin
- Virtual representations of physical products or assets.
- Level 3: System Twin
- These represent entire systems or ecosystems, embracing multiple interconnected components, processes, and stakeholders.
- Level 4: Digital Process Twin
- Replicates the behaviour and dynamics of complex processes or systems, such as manufacturing processes, supply chains, or industrial operations.
Application: industrial uses of digital twins
Automotive/transportation: driving innovation with digital vehicle twins
Digital vehicle twins allow engineers to analyse how different factors such as aerodynamics, fuel efficiency, and safety features impact the overall performance. By simulating various driving conditions and scenarios, engineers can identify potential issues, refine designs, and improve the reliability and safety of vehicles.
The twins also enable predictive maintenance and condition monitoring of vehicles, enabling fleet operators to anticipate maintenance needs, minimize downtime, and optimize asset utilization.
Telecommunications: efficient networks and better customer experience
Telecommunication companies use digital network twins to create virtual replicas of their infrastructures, including towers, antennas, switches, and cables. These digital twins simulate network behaviour, traffic patterns, and performance metrics, enabling operators to identify bottlenecks, predict capacity requirements, and optimize resource allocation.
By integrating real-time data from network elements, sensors, and customer interactions, digital network twins provide operators with actionable insights into network health, enabling proactive maintenance, fault prediction, and service restoration.
Construction: building the future with BIM
In the construction industry, digital twins are known as Building Information Models (BIM). BIMs are a digital representation of a building or infrastructure project that mirror the geometry, spatial relationships, and other relevant data.
Digital twins of construction projects enable architects, engineers, and contractors to collaborate more effectively, visualize designs in 3D, and identify potential conflicts or errors before the start of construction. By simulating construction processes and sequencing activities, BIMs help to optimize project schedules, reduce costs, and improve project efficiency overall.
Medicine: personalized proactive patient care with digital health twins
Digital health twins are virtual representations of individual patients. They enable clinicians to tailor treatment plans and interventions to the patient’s unique medical history, genetic makeup, and lifestyle factors.
By analysing data from wearable devices, electronic health records (EHRs), and medical imaging, clinicians can identify trends, detect early warning signs, and intervene proactively to prevent adverse health outcomes.
Pharmaceutical companies can leverage digital twins to simulate drug interactions, predict drug efficacy, and identify patient subpopulations for targeted therapies, leading to more efficient drug discovery and development processes.
Smart cities: optimizing urban systems with citywide twins
In smart cities, digital twins are known as citywide twins. By modelling transportation networks, energy grids, water systems, and other critical infrastructure, citywide twins help to identify inefficiencies, anticipate future needs, and develop strategies for sustainable growth. They also support resilience to and preparedness for disasters by modelling the impact of natural catastrophes, pandemics, and other crises. In addition to which they also facilitate citizen engagement and participatory planning by providing interactive platforms for residents to explore urban data, provide feedback, and contribute to the development of their communities. By fostering transparency and collaboration, citywide twins empower citizens to play an active role in shaping the future of their cities.
Astronomy: exploring the cosmos with virtual observatories
Digital twins of telescopes allow astronomers to test different configurations, calibrate instruments, and optimize their performance before conducting actual observations. In addition, virtual observatories can integrate data from multiple telescopes and sensors, enabling astronomers to correlate observations and detect hidden patterns in the vastness of space.