Challenges and objectives

The European authorities are already putting progressive pressure on the automotive industry by imposing strict CO2 emission levels. Consequently, manufacturers must commit to respecting a level of 95g CO2/km, knowing that in 2018 the average in Europe was 120.5g CO2/km. While alternative engine solutions (hybrid or electric) are expanding rapidly, making vehicles lighter is also a way to reduce CO2 emissions. It is estimated that a decrease of 100kg will result in a reduction of 10g of CO2/km. This would make it possible to reverse the trend which has led to a continuous increase in the mass of vehicles in recent decades, notably linked to the increase in the performance levels required in terms of safety, comfort, quality, driving, and habitability. We understand the importance of digital simulation for projects combining a multitude of design constraints. In addition, the computational solutions make it possible to dimension the structures as accurately as possible.

Thus, the improvement of existing solutions is done gradually by taking into account the recommendations of each discipline (crash, NVH, etc.).

In a process of continuous technological improvement, our engineering teams propose to rethink this manual multi-physical optimisation by qualifying a method making it possible to deal with the problem as a whole, and taking into account all the constraints in the search for solutions. Thanks to automation, the time saved by computational engineers will be reinvested in a well-thought-out definition of the optimisation problem (variables, loading cases, objectives, and constraints), as well as in the analysis and use of the data generated by the optimisation. This method will therefore make it possible to make the best use of the available computational resources. The objective is to meet the new demands of the industrial sector, namely:

  • Reduced development time
  • Cost reduction
  • Improved performance

Systematic optimisation makes it possible to orient the design towards a solution satisfying all the performance characteristics while minimising the cost and the mass of the system evaluated.


Multidisciplinary optimisation of automotive body-in-white (BIW) 

CIMES services

  • Multidisciplinary optimisation for automotive body-in-white weight reduction
  • Definition of optimisation problem: design space, load cases, objectives, and constraints
  • DOE (Design of Experiments) qualification
  • Automation of computational processes and optimisation of computation times on HPC cluster
  • Analyses of data generated by optimisations 
  • Service and crash analyses (passive safety) including compliance with all criteria related to the safety of vehicle occupants and pedestrians 
  • NVH performance and analyses, responsible for validating the vibratory behaviour and all body stiffness (overall bending stiffness, overall torsional stiffness, strategic local stiffness) 
  • Sustainability performance, responsible for validating fatigue behaviour
  • Internal coordination of business expertise


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Keywords: body-in-white, material weight reduction, multidisciplinary optimisation, robustness, high-performance computing cluster, process automation, response surface, meta-model, safety, crash, durability, NVH, finite element simulation.

Tools: ALTAIR Hyperworks, Ansys, modeFRONTIER, Magna FEMFAT, Catia V5