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A generalized normal flow method with online step controls for pushover analysis of nonlinear softening structures

机译:具有在线阶跃控制的广义法向流动法用于非线性软化结构的推覆分析

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This paper introduces a robust path-following method for pushover analysis of softening structures due to geometric and/or material nonlinearities. The proposed method is a generalized form of the conventional normal flow method (NFM) that provides dimensional consistency in the correction iterations and improves the convergence properties of the algorithm. The generalized NFM is further enhanced by two online step control processes, namely, the Convex Step-size Control (CSC) process and the Hyper Plane Step Control (HPSC) process. The CSC process introduces a set of inequality constraints on the step size to form a convex solution space and (i) enhance the convergence properties of the algorithm by preventing overshooting in highly nonlinear areas, and (ii) improve robustness of the path-tracing process by controlling the resolution of the traced equilibrium path (or spacing/distance between successive equilibrium points) making it unlikely to miss (highly nonlinear) sections of the path. The HPSC process introduces a hyper-plane boundary in the solution space to prevent backward path-tracing. The performance of the proposed generalized NFM with the CSC and HPSC processes is validated through examples with structures exhibiting snap-back and snap-through response due to geometric and material nonlinearities. The proposed method is also compared with existing path-following methods typically used for the pushover analysis of nonlinear structures. (C) 2017 Elsevier Ltd. All rights reserved.
机译:本文介绍了一种鲁棒的路径跟踪方法,用于对由于几何和/或材料非线性而引起的软化结构进行推覆分析。所提出的方法是常规法向流方法(NFM)的一般形式,可在校正迭代中提供尺寸一致性并改善算法的收敛性。广义的NFM通过两个在线步骤控制过程得到进一步增强,这两个步骤分别是凸步长控制(CSC)过程和超平面步骤控制(HPSC)过程。 CSC过程在步长上引入了一组不等式约束,以形成凸解空间,并且(i)通过防止高度非线性区域的过冲来增强算法的收敛性,并且(ii)改进路径跟踪过程的鲁棒性通过控制所跟踪的平衡路径的分辨率(或连续平衡点之间的间隔/距离),使其不可能丢失(高度非线性)路径的各个部分。 HPSC流程在解决方案空间中引入了超平面边界,以防止向后跟踪路径。通过具有几何和材料非线性的结构表现出快速响应和快速响应的示例,验证了采用CSC和HPSC工艺的广义NFM的性能。还将所提出的方法与通常用于非线性结构的推覆分析的现有路径跟踪方法进行了比较。 (C)2017 Elsevier Ltd.保留所有权利。

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