The engineering industry is alight with the phrase “Digital Twin” — a tool in the armoury of someone tasked with making decisions based on data. But what about when the system you are modelling gets really complicated?
First, why is there such a buzz? Applications are widely varied, but let’s look at one specific case: operation of a large wind farm. In a typical 600MW Offshore Wind Farm, figures suggest that digitalisation can unlock a 2% increase in yield, a 10% increase in asset lifetime and a staggering 15% reduction in operations and maintenance cost, combined worth c.£26m/yr.
This isn’t atypical of case studies where data-driven decision making is used to design, build and operate plant. These kinds of figures are pretty attractive to the top- and bottom- lines of a business, so it’s pretty clear why all the fuss.
Making a commercial argument like the above is a painful-but-necessary part of developing a digitalisation strategy for any company: this is what justifies an investment.
Next, how do we deliver that? Invariably, there are tons of working parts — economic models, optimisers, aerodynamic simulations, predictive ML/AI processes munching sensor data, fatigue analyses, etc etc. Typically, each of these working parts comes from a different team of specialists.
