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.
McLaren Automotive (Lead)
School of Aeronautical and Automotive, Chemical and Materials Engineering & Institute of Digital Engineering UK
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.'
Wednesday 10 March – 10:00-11:30
Join us for our upcoming webinar and find out more about how ADAM is accelerating aerodynamics attribute validation through digital engineering tools such as machine learning.
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An IDE Funded Project