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ADAM – Aerodynamic Design Acceleration through Machine learning

The ADAM project is a McLaren Automotive-led collaborative industry research project that aims to promote acceleration of aerodynamics attribute validation through the use of digital engineering tools such as machine learning.


This project will make use of legacy wind-tunnel test data and CFD simulation results to streamline virtual attribute validation by uncovering trends and correlations between CFD and physical tests results.

Collaborative Partners

McLaren Automotive (Lead)
Loughborough University

School of Aeronautical and Automotive, Chemical and Materials Engineering & Institute of Digital Engineering UK

IDE people

James Williams

Monitoring Officer


£1.1 million
total project spend

The project will also help further increasing confidence in the design and development of new vehicles and key aero components. This will reduce the amount of physical testing required to validate the vehicles' performance. Data will be processed by a machine learning algorithm that will increase confidence in numerical assessments and will also have the potential to guide engineers towards the best design based on an objective evaluation of the desired attributes.

'McLaren Automotive's aim to provide the best aerothermal solution for all of our supercars can be achieved through our vision to virtually design, implement and validate innovative ideas and this project will be a key enabler in fulfilling that.'

Adam Webinar

Discover how ADAM is accelerating aerodynamics attribute validation through digital engineering tools such as machine learning. 


Images © Copyright McLaren Automotive Limited. Reproduction free for editorial use only.

An IDE Funded Project

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