Driving 200+ miles an hour, Formula 1 racing cars built by the UK’s McLaren Group have a lot more in common today with fruit flies (a favorite among researchers with an accelerated lifespan of one to two weeks) than cheetahs (the fastest land animal). The company is applying winning insights from rapid, data-driven, evolutionary models versus relying on engine speed and aerodynamics alone to win races.
Cloud-connected cars are now commonplace in the mainstream connected car market that is forecast to surpass $166 billion by 2025. Meanwhile, the digital twin market is set to grow at a 50% compound annual growth rate, reaching $184.5 billion by 2030. For businesses like the McLaren Group, these two trends are at the core of the conglomerate’s digital transformation and competitive strategy, on and off the track.
A Competitive Differentiator
Like professional basketball, industrial-scale farming, national politics, and global merchandising, auto racing has become a data science. Drivers have much less influence over design innovations in the cars than they used to. Racing car design innovation and racing strategy are now dominated by what McLaren engineers call condition-based insights derived from real-time data feeds from hundreds of sensors in cars and the use of digital twins ― which are virtual models of objects, systems, or processes ― and artificial intelligence (AI) and machine learning (ML) technologies.
Each McLaren Formula 1 car has 150 to 200 sensors that collect and transmit data every 0.001 seconds from the car to the edge network and ultimately to McLaren engineers in Woking, England. The sensor data feeds a variety of digital twins that have totally transformed how McLaren innovates and competes through rapid prototyping and simulation.
Using Data to Generate Simulations
The data transmitted from each car during a race ― along with other information such as ambient and track temperatures ― allows engineers to see how a car, component by component, changes throughout a race. Predictive analytics can foretell a breakdown before it happens. Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics.
In the McLaren factory, the sensor data is streamed to digital twins of the engine and different car components or features like aerodynamics at 100,000 data points per second ― which adds up to over one billion numbers in a two-hour race. Before, during, and after each race, digital twins are used to run hundreds of different scenarios based on making small to large design changes and tweaking racing strategies.
The digital twins at McLaren are also used to run simulations for the design of new parts and then to test them for performance and reliability before they are manufactured and installed in the racing cars. How fast are product changes in Formula 1 racing design? McClaren releases product changes every 20 minutes.
“If you started with the fastest car of the group in the first race of the season and you did no development [using digital twins], by the end of the year everyone would overtake you,” said McLaren Group’s Chief Operating Officer Jonathan Neale. “That’s an indication of the relentless pace of change in Formula 1 racing.”
Get Started with Digital Twins
Digital twin technology is now more accessible and affordable than ever before for all kinds of manufacturing organizations thanks to advances in edge networks, in-memory processing, software containers, transport technologies like 5G, advanced analytics, and artificial intelligence. Using High Performance Computing (HPC) infrastructure, McLaren can run thousands of simulations for R&D, production, and racing. Existing digital twin models can look at what’s happening in real-time and predictive analytics can help understand future potential benefits or pitfalls with designs and strategies.
Beyond manufacturers, digital twins can be used in various industries and smart cities to unify data from previously siloed departments, creating a unified source of truth with which to model, simulate, and experiment. Departments as diverse as finance, sales, marketing, design, manufacturing, and operations can use digital twins to predict maintenance, improve patient satisfaction, understand product usage, adjust pricing, and many other actional insights.
Over the past few years, McLaren has worked with Dell Technologies on a journey to test the limits of digital transformation in Formula 1 racing and to bridge the gap between the physical world and its virtual copy, the digital twin. The collaboration has enabled McLaren Group to successfully apply digital twin technology to their technology consulting business serving customers in industries like healthcare and transportation.
The digital twin is a winning approach for a growing number of companies. It requires sensors designed to collect data or vital areas of functionality, such as energy output, temperature, noise, and vibration. Ingesting and processing sensor data is then handled by a distributed computing platform and HPC-powered analytics that rely on AI and ML to handle massive data sets. While complex, digital twin technologies can deliver significant dividends quickly. Success stories abound in industries including manufacturing, utilities, life sciences, oil and gas, and research environments.
For more information on digital twins, read the McLaren Racing and Dell Technologies case study.
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