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Topology optimization methods based on nonlinear and dynamic crash simulations
Topology optimization for crashworthiness has been investigated during the last years, starting from methods based on linear elastic and static simulations [1] or so-called equivalent static loads (ESL) obtained by a single nonlinear crash simulation with a subsequent optimization loop based on the linear stiffness matrix and the corresponding sensitivities [2, 3]. Both methods do not consider material nonlinearities in their optimization process, which are essential for structural components designed for energy absorption, although it is well-known that plasticity and failure play an important role.
LS-TaSC Product Status
The LS-TaSC Version 3.2 topology and shape design tool is presented, as well as the current development that will be available in the next version. The presentation introduces the multipoint numerical derivatives scheme that allows constrained optimization using the mass fractions and load case weights as variables. This allows constrained optimization using any response or mathematical expressions as constraints or objectives. Additionally, topology optimization of NVH load cases is presented. Application examples illustrate these capabilities.
A Systematic Study on Topology Optimization of Crash Loaded Structures using LS-TaSC
Using topology optimization methods, new structural concepts can be generated. These methods are efficient in the field of structural design, taking into account linear structural properties and linear static loading conditions. Usually the mean compliance is considered, subject to a mass constraint. Therefore, the design space is divided into small volumetric elements (so-called voxels) and the algorithm decides based on an analytical sensitivity for every voxel, is there material or not. After this optimization, the engineer has a good proposal and the possibility for the interpretation and the generation of a CAD model.
Free-Form Shape Optimization using CAD Models
The current state of the art in shape optimization is dominated by approaches utilizing computer-aided design (CAD) to manipulate the shape under consideration. On the contrary, free-form shape optimization approaches have not reached the same industrial acceptance, although for example the Vertex Morphing Method showed with many practical problems promising characteristics like high optimization potential, minimum modeling effort or fast design space exploration (see e.g. [1], [2]). One major reason for this limited popularity of free-form shape optimization techniques is their missing link to CAD being the primary design tool in many industrial branches. More precisely, while it is common practice to discretize an initial CAD-model so that a numerical optimization may be performed, it is far from trivial to reconstruct a CAD-model once the discrete optimal design is found - unless the original CAD parametric is used to modify the shape in the first place. The latter, however, depending on the case, may limit the optimization significantly. In the following, we present a novel workflow which may close the existing gap between free-form shape optimization and CAD.