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2017

MDO Collision/NV/Stiffness Optimization with LS-OPT
LS-DYNA is heavily used to analysis transient phenomenon like car crash and makes a huge achievement about physical simulation in a wide variety of industry. For the goal of LS-DYNA, “one- model, one-code” as solution it give you, a wide variety of function has been developed at each section. Nowadays, LS-DYNA has been developed further and become possible to evaluate Frequency domain analysis and Acoustic analysis as FRF/SSD/Acoustic_BEM/_FEM etc. This paper is intended for MDO (Multidisciplinary Design Optimization) with LS-DYNA and LS-OPT. The object is automotive which has many complicated parts. It is so hard to meet the demand for couple of standard for the safety/NV/strength. LS-DYNA can calculate for not only the crash but strength and NV (noise, Vibration) evaluation. The MDO evaluating some linear analyses simultaneously is the common case, but optimization with combination of both linear and non-linear analysis like car crash would be not so common case. It would be possible for LS-DYNA and LS-OPT to consider this case. So, the purpose of this paper is challenge to this case, which means the confirmation to benefit and effect to car design process. When MDO with collision consideration is regarded useful for car design process and estimation of performance, the usage of this type MDO would become widely used.
Efficient Global Optimization Using LS-OPT and Its Parallelization
This article presents the implementation of Kriging-based efficient global optimization (EGO) in LS- OPT, which can be used for both unconstrained and constrained optimization. Additionally it proposes a parallelization technique based on a multi-objective formulation that provides multiple sampling choices. Like any surrogate-based method, the proposed method displays some variation in the results depending on the initial DOE. Further investigation is being conducted to reduce this variation, especially for constrained optimization. The methodology for Pareto-based parallel EGO is different for constrained and unconstrained optimization in some respects. Only the methodology and results for unconstrained optimization are presented in this paper. Analytical examples with known global optima are used to demonstrate the efficacy and the efficiency of the algorithm.
Application of Digital Image Correlation to Material Parameter Identification using LS-OPT