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To develop future electrical and autonomous cars, it is important to virtually test the car in real driving circumstances, including on wet road or under heavy rain conditions. It is especially critical to check that no water prevents the sensors of the driving assistance systems or autonomous cars from working properly, that water intrusion does not disturb electrical equipment, and that the driver’s visibility remains good under rain condition. ESI Group has introduced the Finite Point Method (FPM) in Virtual Performance Solution (VPS) as a CFD mesh free module in order to simulate the interaction of water with the car structure. It was initially specialized for tank sloshing and water drain applications for car closures and is now extended to other application fields. The objective is to enable a holistic prediction of the car behavior under realistic driving conditions, using a virtual car prototype.

Models to represent in position situations based upon uniform pressure assumptions are well established and have been used extensively in the automotive industry for more than 15 years. More recently, in the beginning of the year 2000, advanced simulation techniques with Fluid Structure Interaction (FSI) approaches, such as VPS-PAMCRASH/FPM (Finite Point Method) have been introduced in the development of airbag restraint systems. Their main fields of application are Out Of Position (OOP) situations, where the occupant is close to the airbag casing. For these load cases the deployment kinematics of the airbag and local associated pressures play a major role and require modeling precisely the gas flow. Similarly these techniques are used for side airbags like curtain airbags or knee bags where the deployment kinematics are highly dependent upon local pressures on the membrane of the airbag. The turbulence and viscous flow effects cannot be neglected for curtain bags.

The present contribution will present how local micromechanical properties can be used in an industrial way to assess the crash performance of parts made of short fiber reinforced polymers. To this end, local information about the material structure, predicted by a Manufacturing Process Simulation (MPS), is transferred and mapped automatically on the performance composite part model. The homogenization and mapping techniques will be presented for elastic and nonlinear application fields. Short fiber reinforced injected thermoplastics are widely used in the automotive industry in mass production. Reliable prediction of the performance of short fiber reinforced thermoplastics by simulation for statics and crash simulation can be achieved only by accounting for the full manufacturing process coming from process simulation software.

A new artificial intelligence (model order reduction) / finite element coupled approach will be presented for the risk assessment of battery fire during a car crash event. This approach combines standard crash finite element for the main car body with a reduced order model for the battery. Simulation is today used by automotive engineering teams to design lightweight vehicle bodies fulfilling vehicle safety regulations. Legislation is rapidly evolving to accommodate the growing electrical vehicle market share and is considering additional battery safety requirements. The focus is on avoiding internal short circuit due to internal damage within a cell which may result in a fire hazard. Assessing short circuit risk in CAE at the vehicle level is complex as there involves phenomena at different scales. The vehicle deforms on a macroscale level during the impact event.