Digital twins are virtual/digital replicas of physical objects, persons, or processes created using advanced simulation technologies. These replicas are updated in real time and mimic the original object's behavior.
Digital twin technology helps organizations understand how real-world objects behave under different conditions/circumstances without exposing the original object to risk.
This offers organizations crucial information necessary to optimize and enhance operational efficiency across the aerospace, healthcare, and manufacturing industries.
Let’s understand the key components of digital twins:
A physical entity represents the object, process, or system being replicated or modeled. It could be machinery in a factory, a human body, or even an entire city.
This is the visual representation of the physical entity or the original object. It replicates the physical counterpart in real time, retaining its behavior, structure, and characteristics.
Real-time data is transmitted from the sensors or IoT devices installed on the physical entity to the digital model. This constant flow of data is essential to ensure the digital twin accurately reflects the present state of the physical entity.
Advanced tools and algorithms help analyze data from digital twins to assess performance, predict future outcomes, and test multiple sensors. This allows organizations to make well-informed decisions, predict maintenance requirements, and optimize systems.
Let’s discuss the benefits of the digital twin technology:
Enhanced Decision Making
Digital twins can provide a complete visual and digital view of your windmill (assuming that’s our physical entity).
As the data from the physical entity (the windmill) is being transmitted to your digital twin, you can monitor the output of every component and identify potential flaws in real-time, allowing you to make well-informed decisions.
The best part? You can monitor the windmill from anywhere, reducing reliance on manual elements and the potential for errors.
Reduced Downtime and Predictive Analysis
Digital twins help you closely monitor your assets (a manufacturing plant, a healthcare facility, a smart city), allowing you to identify signs of wear and tear before they lead to potential failures.
Digital twins also combine real-time and historical data to identify trends and accurately predict when a device needs maintenance. This helps reduce downtime and ensures the physical entity stays operational for maximum.
Better R&D and Product Development
Utilizing digital twin technology, organizations can develop precise product simulations prior to constructing the actual product. This enables them to conduct more efficient research and development, refine designs, and detect possible issues during the development phase, leading to superior products and reduced revisions.
Improved Efficiency
Digital twins offer you real-time insights about the physical entity. And let’s assume that the physical entity is a smart city.
By implementing a digital twin of a smart city, organizations can monitor the city's traffic, energy usage, and weather patterns. This can enable city planners to make data-driven decisions that improve traffic flow, reduce energy usage, and create more sustainable infrastructure.
Here are some examples of digital twins in practice to help you understand the concept better:
Manufacturing Industry
Rolls-Royce has an IntelligentEngine program that focuses on efficient engines and services. This program uses digital twins to create replicas for the engines they produce and gather data across several parameters from the onboard sensors.
Healthcare Industry
Össur, a leading name in orthopedic and prosthetics solutions, leverages digital twin technology to test and create tailor-made prosthetics for individuals.
Engineers and designers at Össur use virtual models to simulate and analyze different design iterations to create customized solutions and ensure optimal functionality for every patient.
Smart Cities
The virtual Singapore Platform is the digital twin of Singapore, and it helps authorities in countless ways.
For instance, it allows urban planners to assess how infrastructural projects will impact traffic congestion, identify potential roadblocks, and deal with them. Also, using the digital twin, they can analyze and predict the impact of heavy rainfall on drainage systems to deal with the risk of flooding.
Construction Indsutry
Engineers have leveraged digital twins to visualize the finished product in the case of Crossrail, a major infrastructure project in London. In addition, the digital twin helped the engineers monitor the project's real-time progress and identify potential issues.
In addition to the benefits, digital twins bring several challenges, including:
High Initial Setup Costs and Complexity
Implementing a digital twin can be expensive because it requires advanced IoT devices, sensors, and complex software platforms. Additionally, it may be time-consuming to integrate the new technologies with the existing tech stack,
Dependence on High-Quality, Real-Time Data
Digital twins heavily rely on the quality of data for best performance. If the data is flawed or outdated, it can lead to inaccurate simulations and poor decision-making. Therefore, organizations must invest heavily in data management and real-time data collection, which is challenging.
Security Risks Associated with Data Integration and IoT Devices
Digital twins must be integrated with IoT devices as they supply crucial real-time data. And as these devices are installed on the physical entity, they become prone to data breaches. Imagine how devastating it could be if the hackers manipulated the data for an aerospace project, and it goes unnoticed.
Need for Specialized Expertise to Develop and Manage Digital Twin Solutions
Managing a digital twin solution requires expertise in multiple domains, such as systems engineering, data science, IoT technology, and more. Without qualified professionals, organizations (especially smaller ones) would suffer in setting up digital twins appropriately, leading to inaccurate analysis.
Here are some potential alternatives or extensions to digital twins:
Simulation software helps simulate the physical entity without requiring complete real-time data integration. While this solution is not as powerful as a digital twin (in terms of predictive analysis and real-time monitoring), it’s a more affordable solution.
IoT platforms function like a toned-down version of a digital twin. While they’re not as powerful as their full-scale counterparts, these platforms gather and manage data from IoT devices to create basic-level digital twins, enabling real-time monitoring.
This is more like an extension of digital twin technology. Augmented reality helps organizations interact more engagingly with digital twins by displaying the twin data alongside the physical equipment. This allows engineers to visualize equipment or machines and make even better products.
Digital twins create digital replicas of physical objects. These digital models are connected to their physical counterparts via sensors or IoT devices that collect real-time data.
The collected data is analyzed to monitor the performance, identify/predict bottlenecks, and optimize the operations. This continuous process helps make better decisions, improve efficiency, and reduce risks.
Any industry wherein testing the actual product is extremely costly or resource-intensive and improving is crucial for survival can benefit from digital twin technology. Here are the industries known to benefit from this tech:
Automotive
Manufacturing
Healthcare
Aerospace
Oil & Gas
Transportation
Medicine
Retail
Smart Cities
Shipping and Logistics