Use of Truncated Finite Element Modeling for Efficient Design Optimization of an Automotive Front End Structure

Paper #:
  • 2015-01-0496

Published:
  • 2015-04-14
DOI:
  • 10.4271/2015-01-0496
Citation:
Deb, A., Gunti, R., Chou, C., and Dutta, U., "Use of Truncated Finite Element Modeling for Efficient Design Optimization of an Automotive Front End Structure," SAE Technical Paper 2015-01-0496, 2015, doi:10.4271/2015-01-0496.
Pages:
8
Abstract:
The present work is concerned with the objective of multi disciplinary design optimization (MDO) of an automotive front end structure using truncated finite element model. A truncated finite element model of a real world vehicle is developed and its efficacy for use in design optimization is demonstrated. The main goal adopted here is minimizing the weight of the front end structure meeting NVH, durability and crash safety targets. Using the Response Surface Method (RSM) and the Design Of Experiments (DOE) technique, second order polynomial response surfaces are generated for prediction of the structural performance parameters such as lowest modal frequency, fatigue life, and peak deceleration value. Using the lowest natural frequency of the front end, fatigue factor of safety and peak deceleration extracted from the NCAP crash pulse as constraint parameters, gages of bumper beam, front rails and shotguns as design variables, the mass of the front end structure (i.e. effectively the total mass of the parts mentioned) is optimized. The optimum solution is then obtained by using genetic algorithm functionality in commercial MATLAB package. The stated goal can be achieved by following either of the two different ways: using a Truncated model or a Full Car Model. Running CAE algorithms that include multi-disciplinary areas such as NVH and crash safety using RSM based method using a full car model is very time consuming. The results obtained from truncated model and the full vehicle model are compared and it has been found that the use of truncated model is substantially more efficient when compared to the use of full vehicle model and predicts nearly the same solution as the latter.
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