Machine Learning for Accelerated Aero-Thermal Design in the Age of Electromobility

Eugene de Villiers presents the work by ENGYS developers and engineers within the UPSCALE project and how that relates to future improvements in Aero-Thermal design in the automotive industry.

Artificial Intelligence (AI) coupled with traditional Compute Aided Engineering (CAE) tools provide the potential of transforming the engineering design process. This is one of the driving forces behind the UPSCALE Project, specifically applied to the future design of electric vehicles. The EU project is a collaboration of several industrial and academic partners, where ENGYS is developing components for “Machine Learning Enhanced Simulation Tools”. 

In this webinar, we present how ENGYS is working on enhancing the performance of existing CFD tools using machine learning and model order reduction.



Key Highlights:

• Data-physics Continuum: Or why is everyone so excited about deep learning?

• Introducing UPSCALE: Leveraging data for automotive aero-thermal design

• Progress to Date: Process and methods

• Final Comments



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Meet your Presenter:

Eugene De Villiers - Managing Director, ENGYS

Eugene earned his M.Sc. in Mechanical Engineering from Stellenbosch University (1998) and a PhD in Fluid Dynamics from Imperial College London (2002). His focus areas include numerical modeling, computational fluid dynamics (CFD), and fluid mechanics. Eugene’s journey includes co-founding ENGYS and playing a pivotal role in the company’s remarkable growth from 4 to over 50 people. His leadership has been instrumental in steering numerous successful projects in CFD simulation, showcasing his proficiency in numerical simulation, modeling, and turbulence.

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