Let’s take a look at simulation and digital twins in more detail…
Simulations are used across industry to test products, systems, processes, and concepts. Often used during the design phase, simulations are often digital models using computer-aided design software applications. These models can be created in 2D or 3D to represent parts of a process or product, although they can also be created using mathematical concepts rather than computer-based models. The simulation works by introducing and testing different variables into the digital environment or interface to assess outcomes.
A digital twin is a virtual model that is created to accurately reflect an existing physical object. The physical object is fitted with sensors that produce data about different aspects of the object’s performance, for example on a wind turbine. This data is then relayed to a processing system and applied to the digital model. This digital model, or twin, can then be used to run simulations, study current performance and generate potential improvements that can then be applied back to the actual physical asset. A digital twin can also be created for non-physical processes and systems, mirroring the actual process or system and allowing simulations to be run based on real-time data.
The data used by digital twins is usually collected from Internet of Things (IoT) enabled devices, allowing for the capture of high-level information that can then be integrated into the virtual model.
A digital twin is, in effect, a virtual environment where ideas can be tested with few limitations. With an IoT platform, the model becomes an integrated, closed-loop twin that can be used to inform and drive strategy across a business.
While simulations and digital twins both use digital models to replicate products and processes, there are some key differences between the two. The most notable is that a digital twin creates a virtual environment able to study several simulations, backed up with real-time data and a two-way flow of information between the twin and the sensors that collect this data. This increases the accuracy of predictive analytical models, offering a greater understanding for the management and monitoring of products, policies and procedures.
These differences can be further explained as follows:
- Static vs. Active: A CAD-based simulation models a product or process into which different parameters or design elements can be introduced and tested. This type of model is static as it won’t change or develop unless a designer introduces more elements. However, while a digital twin will begin much the same as a simulation model, the introduction of real-time data means that the twin can change and develop to provide a more active simulation. A digital twin can mature through a product lifecycle as more data is collected and analysed, offering different information that is not available with a static simulation.
- Possible vs. Actual: A simulation replicates what could happen to a product, but a digital twin replicates what is happening to an actual specific product in the real world. Any changes to a simulation are limited to the imagination of a designer who needs to input any changes. However, because a digital twin offers real feedback, the designer can see if it is working as intended and then determine any improvements based on actual use. This translates from assets to other applications, such as for a manufacturing process, which can be assessed with real data to react to changing demands, requirements or business conditions. The difference is that while a simulation is theoretical, a digital twin is specific and actual.
- Scope of Use: The final key difference is the scope of use that is offered by simulations vs digital twins. A CAD-based simulation allows designers to test different scenarios against set parameters, making it useful for product design purposes. However, the scope of a digital twin reaches much further to include all stages of a product’s lifecycle and meaning that the uses are only limited to the data when matched against areas of the business workflow. This increased scope means that digital twin can find uses outside of design and can help improve processes and make wider business decisions.
|
|
Simulation
|
Digital twin
|
|
Data elements and interaction
|
Static
|
Active
|
|
Simulation basis
|
Potential parameters input for testing
|
Real-world feedback from a specific product/process
|
|
Scope
|
Narrow – primarily design
|
Wide – cross-business
|
Digital twin offers a more versatile and deeper simulation process to that offered by CAD-based simulations. These advantages include being able to gather genuine real-time data on an asset’s continued performance across a lifecycle. A digital twin is also not reliant on a designer’s ability to think of and then test any potential parameters, leaving the designer to focus on addressing any genuine issues and coming up with improvements.
A digital twin can also be employed as a tool to inform wider business decisions and are only limited by the scope of the data that is being provided. In addition, through the use of the IoT, digital twins can share data between different systems to provide a clearer picture of performance or for comparison purposes.
The advantages of digital twin over a more basic, non-integrated CAD-based simulation are evident for monitoring valuable products such as wind turbines. However, digital twins can be costly, requiring the fitting of sensors and their integration with analytical software and a user interface. For this reason, digital twins are usually only employed for more critical assets or procedures, where the cost is justifiable to a business.
To better understand the difference between simulation and digital twin it is useful to look at some real life case studies.
For example, while an advanced simulation can analyse thousands of variables a digital twin can be used to assess an entire lifecycle. This was demonstrated by Boeing, who integrated digital twin into design and production, allowing them to assess how materials would perform throughout an aircraft’s lifecycle. As a result, they were able improve the quality of some parts by 40%.
Tesla also use digital twins in their vehicles to capture data that can be used to optimise designs, enhance efforts to create autonomous vehicles, provide predictive analytics and deliver information for maintenance purposes. This actual, rather than theoretical, two-way flow of data could lead to a future where a vehicle could deliver data directly to a garage ahead of a service detailing performance statistics, parts that have been replaced, service records, and potential problems picked up by the sensors. This would deliver time and cost savings as a mechanic could hone in on any problems based on the data rather than having to fully inspect each vehicle for problems.
Where simulations can help you to understand what may happen in the real world, digital twins allow you to compare and assess what may happen alongside what is actually happening.
This real-time view is presented in a clear 3D format making it easy to monitor and interpret the status of projects, equipment, assembly lines and even patients in hospital. This joined-up method of presenting real-time insights can prevent poor decision-making, assist with preventive maintenance and reduce accidents or costly downtimes.
The digital transformation of processes or assets can help improve product design, enhance troubleshooting, and offer new ideas. This is dependent on the quality of the data , which is where ‘smart’ devices and technology come into play, providing a reliable two-way flow of data. This collection of data provides insights into new ways to optimise the real world, whether that involves products, processes and even human-machine interactions.
Related Frequently Asked Questions (FAQs)