Everyone wants to run more simulations, faster and more accurately, but speed and accuracy are usually computationally incompatible. Crash simulations are a case in point, small element sizes are required to capture local buckling etc. and, as a consequence, small stable time increments lead to long run times. Such long runtimes preclude the use of iterative strategies such as DOE or optimization to find improved design proposals. During this webinar Jing Bi, Senior Portfolio Technical Specialist, CSO Structure, SIMULIA will present a new “patent pending” method that accelerates finite element explicit crash simulations by one or two orders of magnitude to enable multi-disciplinary concept and optimization studies.
A coarser mesh would drastically reduce runtime, but with a severe penalty of a loss of accuracy. A hypothesis was proposed that this discretization error could be corrected based on geometry and material properties. A machine learning algorithm was used to determine the correction factors for the coarse mesh representations of 100’s of scenarios, such that outputs (reaction forces, deformations and absorbed energy) of the fine mesh matched the coarser mesh within a 15% error bound.
To illustrate the approach it was applied to a conceptual early design stage automotive crash structure which contained around 20 components, undergoing a 55 km/h frontal crash. The objective was to reduce impact accelerations to 35 g and limit the structural deformations to 550 mm; a complex problem as changes in a single component can change the collapse sequence of the beams, thus creating a bifurcation in the crash response. The coarse mesh approach, along with the Adaptive DOE technique that was used, allowed the optimization process to be reduced from a month to less than an hour.
The webinar will highlight key topics which will be of interest to all engineers:
- optimization of energy absorption structures to meet design requirements
- how machine learning can be applied to classic finite element problems
- the importance of engineering mechanics within any simulation and optimization process
- future simulation trends to support design optimization
REGISTER FOR THE WEBINAR HERE
12:00 - Webinar Begins
13:00 - Webinar Ends
Jing Bi: Senior Portfolio Technical Specialist, CSO Structures, SIMULIA
Jing Bi is a Senior Portfolio Technical Specialist at Dassault Systèmes SIMULIA focusing on machine learning technologies and structural mechanics.
She received her MS and PhD degree in Mechanical Engineering from the University of North Carolina at Charlotte in 2010 and 2012 respectively.
She joined Dassault Systèmes in 2012 and since then worked in a variety of technical roles at SIMULIA and engagement with key customers and partners in additive manufacturing, composites, multiscale modeling and crashworthiness.
In the recent years, she has been developing solutions to accelerate the adoption of machine learning technologies for modeling and simulations.
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