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EFFICIENT SENSITIVITY ANALYSIS FOR MULTIBODY DYNAMICS SYSTEMS USING AN ITERATIVE STEPS METHOD WITH APPLICATION IN TOPOLOGY OPTIMIZATION

机译:使用迭代步骤方法在拓扑优化中应用迭代步骤方法的多体动力学系统的高效灵敏度分析

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Efficient and reliable sensitivity analyses are critical for topology optimization, especially for multibody dynamics systems, because of the large number of design variables and the complexities and expense in solving the state equations. This research addresses a general and efficient sensitivity analysis method for topology optimization with design objectives associated with time dependent dynamics responses of multibody dynamics systems that include nonlinear geometric effects associated with large translational and rotational motions. An iterative sensitivity analysis relation is proposed, based on typical finite difference methods for the differential algebraic equations (DAEs). These iterative equations can be simplified for specific cases to obtain more efficient sensitivity analysis methods. Since finite difference methods are general and widely used, the iterative sensitivity analysis is also applicable to various numerical solution approaches. The proposed sensitivity analysis method is demonstrated using a truss structure topology optimization problem with consideration of the dynamic response including large translational and rotational motions. The topology optimization problem of the general truss structure is formulated using the SIMP (Simply Isotropic Material with Penalization) assumption for the design variables associated with each truss member. It is shown that the proposed iterative steps sensitivity analysis method is both reliable and efficient.
机译:高效可靠的敏感性分析对于拓扑优化至关重要,特别是对于多体动态系统,因为大量的设计变量和求解状态方程的复杂性和费用。该研究解决了与多体型动态系统的时间依赖性动态响应相关的设计目标的一般和高效的敏感性分析方法,包括与大型平移和旋转运动相关的非线性几何效应。基于差分代数方程(DAES)的典型有限差分方法,提出了一种迭代敏感性分析关系。对于特定情况,可以简化这些迭代方程以获得更有效的灵敏度分析方法。由于有限差分方法是一般的并且广泛使用,因此迭代敏感性分析也适用于各种数值解决方案方法。考虑到包括大型平移和旋转运动的动态响应,使用桁架结构拓扑优化问题来证明所提出的敏感性分析方法。使用SIMP(简单的各向同性材料与惩罚)假设与每个桁架构件相关联的设计变量的假设配制了一般桁架结构的拓扑优化问题。结果表明,所提出的迭代步骤敏感性分析方法既可靠且有效。

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