How Advanced Simulation Drives Digital Twin Implementation

Aug. 31, 2022
The distinct advantage of digital twins is their ability to collect and analyze authentic real-time data from an asset’s continual operation across its lifecycle.

Digital twins are increasingly being used to help inform wider business decisions as well as fundamentally changing business models. And, when implemented in an IIoT (Industrial Internet of Things) environment across a digital thread, digital twins can share data between different systems, providing a clearer and more precise picture of performance on a larger scale, such as entire factories and supply chains.

Where simulation can provide the means to understand what may happen in the real world, a digital twin allows engineers and operations technicians to compare what may happen concurrent with what is happening in real time. The digital transformation of production processes or assets in the field can enhance product design, optimize asset performance, and provide insights into building the next generation of systems and factories. However, this is very much dependent on the quality of the data, and the level of intelligence in smart edge devices and sensors.

Digital twins are components of larger IIoT ecosystems based on a multi-tiered architecture that ranges from enterprise-level cloud network infrastructure to edge computing services and platforms, and culminates at the far edge of intelligent devices and IIoT endpoints. Therefore, digital twins are dependent on the digital thread supported by this multi-tiered architecture.

Key differences
The underlying simulation technology used for product testing and validation (computer aided engineering—CAE) is shared with the virtual modeling of physical production systems and assets in the field to implement a digital twin. Although both applications share the ability to execute virtual simulations, they are not the same. While the simulation capability of today’s integrated design/ test platforms is very powerful and has significantly accelerated the product development process, the capabilities of a digital twin extend well beyond product development. In both applications the simulation is executed using a virtual model, but the model becomes a digital twin only after the product is produced.

When implementing a digital twin, users quickly understand that it needs to function within an IIoT environment with a working digital thread across the product/production lifecycle. A digital twin operating in this environment can receive real-world data quickly and process it, allowing the product designer or manufacturing engineer to virtually see how the real product, equipment, or asset is operating.

While virtual simulation—for both product testing and digital twins—uses virtual models to replicate product functions and production processes, there are some key differences in application of the two instances. CAE simulation applications usually determine if a product meets design criteria for fit, form, and function. Conversely, in the implementation of a digital twin, a virtual environment is created where engineers can study multiple simulations backed up with real-time data and two-way flow of data between the digital twin models and the sensors and intelligent end devices that collect the data from the asset(s). The result is more accurate predictive/prescriptive analytics that drives the optimization and enhanced operational understanding of products and assets.

A product test/validation simulation typically uses CAE applications such as finite element analysis, multi-physics, and computational fluid dynamics to create a simulation model into which the designer can introduce and test various design elements. The resulting computer aided design (CAD) model is basically static until the designer introduces new elements.

The virtual aspect of a digital twin can begin with the creation of CAD. However, the real value of a digital twin is realized when the virtual models receive real-time data from its physical world counterpart. At this point the digital twin simulation becomes active and the model changes as real-world data is received. The dynamic nature of the digital twin, based on constantly changing data, gives it the ability to mature through the product lifecycle, as well as drive business decisions based on the maturing and improvement of the product.

Adoption of digital twins is currently gaining momentum across a variety of industries, especially as suppliers that offer comprehensive closed loop digital twin platforms and technologies have emerged. Embarking on a digital twin journey can look very daunting initially, given the advanced simulation technology involved, the operational and infrastructure requirements, and the clear definition of use cases you want to focus on. These use cases can involve the entire enterprise landscape, including product development, manufacturing processes, and a business model development process. Companies should research and assess suppliers that can offer a comprehensive and integrated digital twin that includes proven simulation technology, IIoT connectivity, and accompanying infrastructure architecture.

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