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Bi-directional evolutionary topology optimization of geometrically nonlinear continuum structures with stress constraints

机译:具有应力约束的几何非线性连续体结构的双向演化拓扑优化

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This paper proposes a design method to maximize the stiffness of geometrically nonlinear continuum structures subject to volume fraction and maximum von Mises stress constraints. An extended bi-directional evolutionary structural optimization (BESO) method is adopted in this paper. BESO method based on discrete variables can effectively avoid the well-known singularity problem in density-based methods with low density elements. The maximum von Mises stress is approximated by the p-norm global stress. By introducing one Lagrange multiplier, the objective of the traditional stiffness design is augmented with p-norm stress. The stiffness and p-norm stress are considered simultaneously by the Lagrange multiplier method. A heuristic method for determining the Lagrange multiplier is proposed in order to effectively constrain the structural maximum von Mises stress. The sensitivity information for designing variable updates is derived in detail by adjoint method. As for the highly nonlinear stress behavior, the updated scheme takes advantages from two filters respectively of the sensitivity and topology variables to improve convergence. Moreover, the filtered sensitivity numbers are combined with their historical sensitivity information to further stabilize the optimization process. The effectiveness of the proposed method is demonstrated by several benchmark design problems.
机译:本文提出了一种设计方法,可在体积分数和最大冯·米塞斯应力约束下最大化几何非线性连续体结构的刚度。本文采用扩展的双向进化结构优化方法。基于离散变量的BESO方法可以有效地避免基于密度的低密度元素方法中众所周知的奇点问题。冯·米塞斯(von Mises)的最大应力由p范数整体应力近似得出。通过引入一个拉格朗日乘数,传统刚度设计的目标通过p范数应力得以增强。拉格朗日乘数法同时考虑了刚度和p范数应力。提出了一种确定拉格朗日乘数的启发式方法,以有效地限制结构最大冯·米塞斯应力。通过伴随方法详细推导了用于设计变量更新的敏感性信息。对于高度非线性的应力行为,该更新方案分别利用了两个滤波器的灵敏度和拓扑变量的优势,以提高收敛性。此外,将滤波后的灵敏度数字与其历史灵敏度信息结合在一起,以进一步稳定优化过程。几个基准设计问题证明了该方法的有效性。

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