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Make an impact in the real world with Digital Twins

Let's discuss the importance of keeping your digital twins in sync with real products. Models so accurate they’re a digital twin of the product you’re creating can help you throughout the design and development process. 1D models can help you determine the best architecture for your multi-physics system, 3D models can help you design into the details, and testing can help you improve modelling realism. Combining all the three technologies gives you the highest possible accuracy while making design decisions. But in today’s world the design job isn’t done when you ship. You have to be able to take feedback, track how products are used – with detailed data coming from the increasing numbers of sensors in modern devices – and then use that to support, maintain and improve the products you have out in the market, as well as incorporating all that information into your next generation. That means keeping your digital twins in sync with the real products, even once they’re in customers’ hands.

Depending on what you’re designing, your engineering process may include systems simulations, finite element analyses of components and assemblies to understand stress, dynamics, and failure, computational fluid dynamics to analyse fluids and thermal properties, multibody dynamics to represent motion behaviour, and test-based methods that augment your simulations. This brings in the opportunity to use predictive engineering analytics – to combine data from simulations, benchmarks, prototype tests and even usage data from existing products to help you better predict the performance of your design. Increasingly, you’ll want to explore the whole design space, changing tens or even hundreds of parameters at a time and visualising the right combinations of those parameters to experiment with, so you can make key architectural decisions early on, track important parameters throughout the process, and then efficiently optimise performance as you work through the design.

A simple, static model doesn’t store enough information to let you perform such predictive engineering analytics, or to help you with complex support questions after you ship the product, or to be the basis for engineering follow-up products. For that you need a digital representation that can predict all the ways a product will perform, at each step of development and on into actual usage; a digital twin of the physical product.

As you evolve this digital twin, you’ll want to correlate the data that you measure in tests, with the data that’s predicted by the model in your simulation, over several cycles of simulation and test, so you can make sure the two are converging. That’s what gives you confidence that the model is an accurate representation of the product you’re designing and building.

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