Descrizione del progetto
Modelli di apprendimento automatico predittivo per la valutazione di incidenti e aerodinamica
Il calcolo ad alte prestazioni (HPC, High-Performance Computing) e l’ingegneria assistita da computer (CAE, Computer-Aided Engineering) svolgono un ruolo fondamentale nel processo di sviluppo dei veicoli. Del totale dell’utilizzo dell’HPC nel settore automobilistico, circa il 20 % è destinato alle simulazioni aerodinamiche e termiche e fino al 50 % delle risorse HPC alle simulazioni di incidenti. Il progetto UPSCALE, finanziato dall’UE, integra metodi di IA direttamente nel software CAE tradizionale basato sulla fisica e nei metodi utilizzati nello sviluppo del trasporto stradale in tutto il mondo. Il progetto si concentra sull’applicazione di metodi di IA per ridurre i tempi di sviluppo e aumentare le prestazioni dei veicoli elettrificati, riducendo così i livelli di emissioni a livello globale. UPSCALE ha scelto come casi d’uso del progetto le due applicazioni CAE a maggiore intensità di HPC: la modellizzazione aerodinamica/termica dei veicoli e la modellizzazione degli incidenti.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
43710 Santa Oliva
Spagna