Advertisement

Current CAE Trends in the Automotive Industry

  • Vasileios TsiolakisEmail author
  • Henry P. Bensler
Chapter
  • 63 Downloads
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 54)

Abstract

This paper provides a compact review of the current Computer Aided Engineering (CAE) trends in the automotive industry from the perspective of Volkswagen Group research. CAE has established itself as an integral part of the vehicle engineering design process. Moreover, it provides the foundation for success in a very competitive market. In order to reduce costs and time to market, the number of physical prototypes needs to be reduced. This can only be accomplished through the systematic advancement of virtual methodologies that support the brands to evolve towards a 100% simulation-based prototype-free product development environment. Several relevant areas of vehicle development are considered. Starting with the issue of safety, the topics of finite-element human body modelling for occupant and pedestrian safety evaluation as well as structural vehicle crash simulations are discussed. Other topics such as the usage of computational fluid dynamics methods in the areas of vehicle aerodynamics and aeroacoustics are also considered. Finally, new areas of methods development are briefly discussed by showing novel applications of reduced order modelling and artificial intelligence methods including Big Data analysis. These methodologies will provide the basis for greatly accelerating solver speed and ensure the extraction of more information from simulation results.

Keywords

Automotive industry Computer aided engineering Reduced order modelling Artificial intelligence Big data 

References

  1. 1.
    Díez P, Zlotnik S, Huerta A (2017) Generalized parametric solutions in Stokes flow. Comput Methods Appl Mech Eng 326:223–240MathSciNetCrossRefGoogle Scholar
  2. 2.
    Evans D, Hartmann M, Delfs J (2018) Tailerd Green’s function beamforming in numerical data. In: DAGA MünchenGoogle Scholar
  3. 3.
    Kabet vel Job A, Hartmann M, Sesterhenn J (2016) Prediction of the interior noise level for automotive applications based on time-domain methods. In: INTER-NOISEGoogle Scholar
  4. 4.
    Othmer C (2014) Adjoint methods for car aerodynamics. J Math IndGoogle Scholar
  5. 5.
    Yaici W, Entchev E (2016) Coupled computational fluid dynamics (CFD) and artificial neural networks (ANNs) for prediction of thermal-hydraulic performance of plate-fin-tube heat exchangers. In: COMSOLGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Volkswagen AGWolfsburgGermany
  2. 2.Laboratori de Càlcul Numèric (LaCàN)Universitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.Zienkiewicz Centre for Computational Engineering, College of EngineeringSwansea UniversitySwanseaUK

Personalised recommendations