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.View Article