Direct Load-Carrying Boundary Identification-Based Topology Optimization Method for Structures With Design-Dependent Boundary Load

IF 2.7 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Boyuan Fan, Huixin Huang, Jingyu Hu, Shutian Liu
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引用次数: 0

Abstract

During topology optimization with design-dependent boundary load, updating the load conditions is necessary. However, it is challenging to identify the load-carrying boundary in density-based topology optimization frame. To address this issue, a direct load-carrying boundary identification method is proposed to describe and update the design-dependent boundary load, and a topology optimization method for structures with design-dependent boundary load is presented. First, a Flood Fill algorithm (FFA) based domain extension method is introduced to generate a new structure with a boundary equivalent to the load-carrying boundary of the original structure. Then, the erosion boundary identification method is applied to the new structure to identify the load-carrying boundary instead of the original structure. Finally, the load information (direction and magnitude) of the design-dependent boundary load is determined using a normalized gradient algorithm, which completes the update of the design-dependent boundary load. This method overcomes the difficulty of identifying the load-carrying boundary in density-based methods. The effectiveness of this method is demonstrated by several examples of minimum compliance (including 3D) and flexible mechanisms.

基于直接承载边界识别的设计依赖边界荷载结构拓扑优化方法
在设计依赖边界荷载的拓扑优化过程中,需要对载荷条件进行更新。然而,在基于密度的拓扑优化框架中,承载边界的识别是一个挑战。为了解决这一问题,提出了一种直接承载边界识别方法来描述和更新设计依赖边界荷载,并提出了一种具有设计依赖边界荷载的结构拓扑优化方法。首先,引入基于洪水填充算法(FFA)的区域扩展方法,生成与原结构的承载边界等效的新结构;然后,将侵蚀边界识别方法应用到新结构中,代替原结构识别荷载边界;最后,利用归一化梯度算法确定设计依赖边界荷载的载荷信息(方向和大小),完成设计依赖边界荷载的更新。该方法克服了基于密度的方法中识别承载边界的困难。通过几个最小柔度(包括3D)和柔性机构的实例证明了该方法的有效性。
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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
276
审稿时长
5.3 months
期刊介绍: The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems. The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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