BIMScaler Blog – In the fast-changing world of Industry 4.0, the lines between digital twin vs simulation are becoming increasingly blurred.
While both technologies are great for understanding and optimising complex systems, they have different capabilities and applications.
So, let’s take a closer look at these similar but different technologies.
Digital Twin Definition
So, what is a digital twin? A digital twin is a virtual representation of a physical entity or system you can update as things change.
It reflects the structure, behaviour and data of the real-world counterpart, so you can monitor, analyse and predict in real time.
The link between the physical and digital entities is made through a constant stream of data, often helped by sensors and IoT devices.
This relationship is symbiotic, meaning it’s a two-way street.
The digital twin can evolve alongside its physical counterpart, giving valuable insights into how it’s performing, how healthy it is, and what its potential future states might be.
The ABB paper “Review: Digital Twins and Simulations,” shows how digital twins can transform industries like manufacturing, energy, healthcare, and aerospace.
Meanwhile, Ana Wooley and colleagues in “When is a Simulation a Digital Twin? A Systematic Literature Review,” shows how digital twins are different from simulations because they have a two-way data flow that lets the digital model change as the real-world thing does.
One of the first examples is the NASA-developed spacecraft digital twin.
This shows how such models can be used to replicate physical systems for design changes and maintenance predictions without the need for physical prototypes.
What is Simulation?
Simply put, simulation is usually a one-off digital model used to copy and study how a system behaves in certain situations.
The simulation lets you experiment and analyse without building physical prototypes or testing them in the real world.
While simulation can be really useful for understanding how a system works, this technology doesn’t always reflect how the system behaves in real-time.
Wooley et al. found that only 11% of the literature on digital twins used simulations to their full potential.
This shows a gap between what we know theoretically and how we do things in practice.
In their paper “Enabling Elements of Simulations Digital Twins and its Applicability for Information Superiority in Defence Domain,” Kapish Aggarwal and team point out how great simulations for things like optimisation and performance testing.
But simulations don’t have the dynamic, real-time feedback integration you get with digital twins.
Aggarwal also points out how AI, optimisation, and cloud computing can help us create smarter digital systems.
It shows how these technologies can be used to develop digital twins that can cut costs, improve flexibility and make real-time decisions, particularly in the defence sector.
What is the Difference Between Digital Twins and Simulation?
Purpose and Scope
The primary difference between simulation vs digital twin lies in their intended use and application range.
Simulations are often used for specific tasks, such as testing different scenarios, improving system performance, or predicting outcomes in a controlled environment.
That’s why ABB highlights how useful simulation can be when trying out different product versions or testing systems in risky situations.
For instance, if you want to test how a system would perform in an earthquake, you can’t really do that with physical tests.
On the other hand, a digital twin is more than just a simulation. It has a live, two-way link with the real thing.
Data Integration and Real-time Updates
Simulations often use historical or pre-defined data sets to power their models.
They can usually handle some level of dynamism, but they typically don’t have the real-time data exchange.
Digital twins, on the other hand, are constantly updated with live data from sensors and IoT devices embedded in the physical system.
This means the digital twin can stay in sync with the real thing, giving you the latest insights and helping you make proactive decisions.
Let’s look at the ABB study case as an example. The digital twins help integrate real-time data from solar inverters and environmental sensors.
This approach lets you track performance accurately, spot anomalies, and do predictive maintenance. It shows how powerful real-time data can be in digital twin applications.
Lifecycle Coverage
When it comes to comparing the lifecycle of digital twin vs simulation, the digital twin is the one that stands the test of time.
Simulation is usually used at specific stages of a system’s lifecycle, like the design or testing phase.
Once the system goes into production, that’s typically when the simulation stops being used.
With digital twins, though, you can use them throughout the entire lifecycle, from design and development to operation, maintenance, and decommissioning.
As the ABB Review explains, digital twins can follow a product through its entire operational life, constantly getting better based on real-world data.
Aggarwal also says that digital twins are used in complex defence systems.
They can simulate and monitor assets from development through to operational deployment, which shortens development cycles and improves system reliability.
Fidelity and Accuracy
When it comes to how close they can get to the real thing, simulations can be pretty detailed.
But, please note, it depends on how complex the model is and what kind of computing power you have.
However, these models are often limited to specific parts of the system being analysed, such as thermal or structural properties.
Digital twins, on the other hand, offer a more comprehensive approach, integrating data from a wide range of physical phenomena to create a more accurate and holistic representation of the asset.
For example, Aggarwal’s paper shows how surrogate models can be used in digital twins.
This makes it possible to combine different inputs to perform faster and more accurate simulations, which ultimately improves the precision of predictions in high-stakes environments like aerospace.
Learn more: High Demand of Unity Digital Twin: Application, Process, & How to Get It
Digital Twin Pros and Cons
Pros
One of the best things about digital twins is how they let you update the virtual model in real time, so it always reflects the current state of the physical thing.
The ABB Review paper shows how this works in practice. It tells the story of a pilot study using digital twins that successfully predicted equipment deterioration 90% of the time.
This meant that maintenance could be carried out on time and there was less downtime.
Furthermore, digital twins can slash development costs and speed up time to market by eliminating the need for physical prototypes and extensive real-world testing.
Then, digital twins make it easy for everyone involved to work together and communicate by providing a shared online space where they can see and interact with the system.
Cons
While digital twins have a lot to offer, they also present a few challenges.
One of the main drawbacks is the high cost of implementation.
Building a fully functional digital twin requires a big upfront investment in sensors, data integration systems, and cloud infrastructure.
Wooley and colleagues said most companies don’t have the money to develop comprehensive digital twins for large-scale operations.
We also need to think about data security and privacy.
Digital twins rely on a continuous flow of sensitive data, which raises concerns about data security and privacy.
Making sure this data is safe from cyberattacks and that we stick to data protection rules is really important.
And we can’t forget about the human element. Many organisations might find it tough to get and keep people with the right skills in areas like data analytics, modelling and simulation.
In addition, getting digital twins to fit into existing workflows and processes might mean big changes for the company’s management and a shift in people’s thinking.
Advantages and Disadvantages of Digital Simulation
Advantages
The great thing about digital simulations is you can experiment with lots of different scenarios without having to do any real-world testing.
Simulation lets engineers try out different scenarios, which helps them make designs, processes, and operations better.
ABB says that being able to run lots of simulations can cut product development times and save money on prototypes.
For instance, in the automotive industry, companies can test vehicle designs against crash scenarios before building a physical prototype.
Another big plus is you don’t have to spend a fortune. Simulation is often a lot cheaper than real-world testing, especially when you’re dealing with large systems or dangerous conditions.
Disadvantages
Once again, unlike digital twins models, simulations don’t integrate real-time data.
This means simulations can’t adapt to changes in the physical system after the initial setup, so they’re not so good for monitoring ongoing processes.
As Wooley et al. point out, simulations are often based on historical data, so they lose accuracy over time unless they’re updated regularly.
Another thing to think about is the computing power needed for high-fidelity simulations.
Complex simulations, especially those involving multiple physical interactions like fluid dynamics or electromagnetism, can take hours or even days to compute.
This can slow down decision-making in fast-paced industries like pharmaceuticals or aviation.
Learn more: Asia Pacific Digital Twin Market: Growth, Trends, and Future Outlook
The Digital Twin vs Simulation: Which One is Better for You?
It’s not about saying one is better than the other. It’s not about the battle between simulation vs digital twin.
It’s about knowing which one is best for your needs and goals.
Both technologies have their own unique advantages, so the best choice depends on the context of your project and what you’re trying to achieve.
If you’re looking to monitor in real-time, predict maintenance needs and optimize the performance of an existing physical system throughout its lifecycle, a digital twin is probably your best bet.
On the other hand, if you’re still in the early stages of design and development, looking to explore different scenarios and get the system to work as well as possible before you start building it, simulation might be a better bet.
It’s important to think carefully about what you need from the technology, what resources you have available and what you want to achieve. Then you can choose the right technology for the job.
That’s Why You Need a Support System in the Adoption Process
So yes, the idea of adopting a digital twin vs simulation model might seem a bit intimidating, especially if your team is already short on resources or expertise.
But don’t worry, taking that first step doesn’t have to be overwhelming.
At BIM Scaler, we’re on a mission to make digital twins or simulation as a reality for Australian businesses like yours.
Our complete range of services is there to make your digital twin adoption as stress-free as possible.
We’ve got the skills to make your digital twin creation process smoother and more efficient. Our expertise lies in clash detection, 4D/5D planning and effective stakeholder communication.
Also, we make sure your digital twin lasts as long as you need it to, so it’s always worth its weight in gold.
The goal is you get a reliable support system you can count on to help you work through the challenges with confidence.
That’s why, whenever you are ready to experience the difference, kindly visit our BIM Management Support page to discover how we can assist you.
Or, let’s grab lunch – no sales, no pushy pitches; just a friendly discussion on how to make your complex digital dreams a reality, one step at a time.
Just drop us a line to arrange a time that works best for you.
In Closing
It doesn’t matter if you choose a digital twin or a simulation; either way, they can help you gain valuable insights, drive innovation, and make things more efficient.
Because it’s beyond digital twin vs simulation. All you need is to consider many factors inside your company when making your choice. That’s why we at BIM Scaler can help you from the early stages.
