Digital twins and IoT #snsinstitutions #snsdesignthinkers #designthinking

Digital twins are virtual replicas of physical devices that data scientists and IT pros can use to run simulations before actual devices are built and deployed. Digital twins can also take real-time IoT data and apply AI and data analytics to optimize performance.

A digital twin is a digital representation of a physical object or system. The technology behind digital twins has expanded to include buildings, factories and even cities, and some have argued that even people and processes can have digital twins, expanding the concept even further.

In essence, a digital twin is a computer program that takes real-world data about a physical object or system as inputs and produces as outputs predictions or simulations of how that physical object or system will be affected by those inputs.

How does a digital twin work?

A digital twin begins its life being built by specialists, often experts in data science or applied mathematics. These developers research the physics that underlie the physical object or system being mimicked and use that data to develop a mathematical model that simulates the real-world original in digital space.

The twin is constructed so that it can receive input from sensors gathering data from a real-world counterpart. This allows the twin to simulate the physical object in real time, in the process offering insights into performance and potential problems. The twin could also be designed based on a prototype of its physical counterpart, in which case the twin can provide feedback as the product is refined; a twin could even serve as a prototype itself before any physical version is built.

However, a digital twin can be as complicated or as simple as you like, and the amount of data you use to build and update it will determine how precisely you're simulating a physical object. For instance, this tutorial outlines how to build a simple digital twin of a car, taking just a few input variables to compute mileage.

Digital twins and IoT

Digital twins can be used to predict different outcomes based on variable data. This is similar to the run-the-simulation scenario often seen in science-fiction films, where a possible scenario is proven within the digital environment. With additional software and data analytics, digital twins can often optimize an IoT deployment for maximum efficiency, as well as help designers figure out where things should go or how they operate before they are physically deployed.

The more that a digital twin can duplicate the physical object, the more likely that efficiencies and other benefits can be found. For instance, in manufacturing, where highly instrumented devices are deployed, digital twins might simulate how the devices have performed over time, which could help in predicting future performance and possible failure.

Digital twins offer a real-time look at what's happening with physical assets, which can radically alleviate maintenance burdens. Chevron is rolling out digital twin technology for its oil fields and refineries and expects to save millions of dollars in maintenance costs. And Siemens, as part of its pitch, says that using digital twins to model and prototype objects that have not been manufactured yet can reduce product defects and shorten time to market.