Project description
ML predictive models for crash and aerodynamics assessment
High-performance computing (HPC) and computer-aided engineering (CAE) play an instrumental role in the vehicle development process. Of the total automotive HPC usage, approximately 20 % goes to aero/thermal simulations, and up to 50 % of HPC resources to crash simulations. The EU-funded UPSCALE project integrates AI methods directly into traditional physics-based CAE software and methods used in developing road transportation worldwide. The project focuses on AI-methods application to reduce the development time and increase the performance of electrified vehicles, thus reducing global emission levels. UPSCALE has chosen as use cases for the project the two most HPC intensive CAE applications: vehicle aero/thermal modelling and crash modelling.
Objective
UPSCALE is the first EU-project that has the specific goal to integrate artificial intelligence (AI) methods directly into traditional physics-based Computer Aided Engineering (CAE)-software and –methods. These CAE-tools are currently being used to develop road transportation not only in Europe but worldwide. The current focus of the project is to apply AI-methods to reduce the development time and increase the performance of electric vehicles (EVs) which are required by the automotive industry to reduce global emission levels. High performance computing (HPC) and CAE-software and –methods play a decisive role in vehicle development process. In order to make a significant impact on the development process, the two most HPC intensive CAE-applications have been chosen as use cases for the project: vehicle aero/thermal- and crash-modelling. When considering total automotive HPC usage, approximately 20% is used for aero/thermal simulations and up to 50% of HPC resources are utilized for crash simulations. By improving the effectiveness of these two areas, great increases in efficiency will lead to a 20% of reduction of product time to market. Other novel modelling approaches such as reduced order modelling will be coupled to the AI improved CAE-software and -methods to further reduce simulation time and ease the application of optimization tools needed to improve product quality. Through the combined effort of universities, research laboratories, European automotive OEMs, software companies and an AI-SME specialized in machine learning (ML), the UPSCALE project will provide a unique and effective environment to produce novel AI-based CAE-software solutions to improve European automotive competiveness.
Fields of science
- natural sciencescomputer and information sciencessoftware
- social sciencessocial geographytransportelectric vehicles
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
43710 Santa Oliva
Spain