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2 Edge Computing: New Support for Digital Twins // / A Key Consideration: Choosing the Right Technology Environment While Ansys makes the choice of a simulation platform easy, companies exploring the digital twin concept also need to consider the larger technology environment in which their working product models will exist. Because these models typically involve complex models, depend on huge volumes of data and consider dynamic performance, they necessarily require large amounts of computational power. Companies investing in digital twins require a robust computing environment that not only supports the connection of the model to the working product via the IIoT, but has the bandwidth and responsiveness needed to process high volumes of real-time performance data. The computing environment also needs to support a response speed great enough to apply insights in a timely manner. Often, there is a performance or maintenance issue identified that is critical, and the operations team needs to be notified immediately. / The Proven Advantages of Cloud Computing Because few companies are willing to invest in on-premise resources to support their digital twin initiatives ― including hardware, software and processors ― cloud computing has quickly become the preferred technology environment for hosting the needed IIoT computational resources. A cloud-based approach is usually cost-effective, because the technology resources are flexible and scalable. As additional processing or storage capacity is needed to scale out operating problems, it can be added easily and seamlessly, without making a long-term commitment. Cloud providers ensure that IT resources always reflect the state-of-the-art, so companies can avoid making their own ongoing investments in physical computing assets. In addition, since cloud hosting is readily available, this approach supports a fast implementation of digital twins via Ansys Twin Builder. The solution can be up and running rapidly, without any concerns about speed, capacity, bandwidth or long-term resource commitments. / Edge Computing: A Promising New Capability While cloud computing has proven successful in supporting the digital twin initiatives of many organizations, today a new capability is emerging. Called "edge computing," this strategy leverages technology resources that are in close physical proximity to the asset that's being monitored. Instead of sending data to a remote location and computing on physically distant IT assets, edge computing eliminates any degree of lag time because it's faster and easier to process information near the source. How fast is edge computing growing? Gartner has predicted that "By 2022, more than 50% of enterprise-generated data will be created and processed outside the data center or cloud." 1 By allocating data collection, processing and analysis to the "edges" of the overall computing network, edge computing decentralizes the workload and avoids using a centralized data center or cloud unnecessarily. While cloud resources are elastic, at times there can be tremendous increases in the volume of requests, especially when hundreds or even thousands of IIoT devices are simultaneously sending data or processing queries. In the case of a digital twin initiative, the real-time information generated by an operating machine ― including temperature, vibration and pressure levels ― is captured by IIoT sensors and then typically converted to a different data format for analysis. If this data is then sent to a cloud, processed and sent back, delays may result. The insights generated may be far from "real time" when they are finally applied. By leveraging edge devices, companies can increase processing speeds, improve data accessibility and eliminate the "noise" and latency that result from transmitting data over great geographic distances to a shared cloud resource. For organizations that are strictly regulated by the industry or the government, edge computing offers an additional advantage. Data security and compliance may be more easily achieved if processing is accomplished via a private, local network of technology resources. Uploading data to a cloud, even a private cloud, may violate regulations and put sensitive data or equipment controls at risk. Phoenix Contact Electronics engineers used Ansys Twin Builder to develop a digital twin of its safety relays that helps predict failures in advance. In creating and managing such a numerically large model, choosing the right technology environment is critical.

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