Achieving Scalability with Digital Twins

Tom Clark
5 min readMar 11, 2020

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.

Generating all this energy for £26m/yr less sure would help us tackle climate change.

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.

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Tom Clark

Fluid Dynamicist at the core, lover of chaos theory. Experienced scientist, developer and team lead working in wind energy — from startups to heavy industry.