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首页> 外文期刊>International journal of mechanics and materials in design >Structural shape optimization with meshless method and swarm-intelligence based optimization
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Structural shape optimization with meshless method and swarm-intelligence based optimization

机译：基于无网格方法的结构形状优化和基于群智能的优化

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摘要

This paper presents a distinctive numerical approach for shape optimization by coupling meshless method with stochastic swarm intelligence based optimization technique for two dimensional linear elastic problems. Element free Galerkin method has been used here for structural analysis to circumvent frequently encountered issues with traditional grid-based technique like FEM in shape optimization such as heavy reliance on quality mesh for accurate solutions needing remeshing due to initial mesh distortion in case of large shape changes, discontinuous secondary field variables across element boundaries needing post-processing techniques and mesh optimization to minimize computational errors. Another distinguishing feature of present work is deployment of gradient-free particle swarm optimization technique for obtaining near optimal solution in shape optimization which eradicates computational efforts and errors associated with sensitivity computation. In this work, for design boundary representation Akima spline has been used due to its better stability against outlier points during shape optimization which generates natural looking boundaries. The performance of proposed technique is validated through numerical examples of shape optimization with behavior constraints on displacement and stress. To demonstrate effectiveness of present technique, results obtained through the proposed technique are compared with other techniques of past literature. To ensure acceptable levels of solution accuracy during shape optimization, h-refinement for initial problem geometry has been carried out as some shapes generated during the optimization process may have low field node density.
机译：本文通过耦合啮合优化的耦合方法，采用随机群智能基于二维线性弹性问题的磁心方法，呈现了一种独特的数值方法。这里已经使用了元素免费Galerkin方法来进行结构分析，以规避常见的基于网格技术的问题，如FEM形状优化，如厚重对质量网格的依赖性，因为在大形状变化的情况下由于初始网格失真而需要回忆。 ，跨元素边界的不连续的次级场变量需要后处理技术和网格优化以最大限度地减少计算错误。目前工作的另一个区别特征是在形状优化中进行近似最佳解决方案的渐变粒子群优化技术的部署，其消除了与灵敏度计算相关的计算工作和错误。在这项工作中，对于设计边界表示，由于其在形状优化期间对异常值点的稳定性更好的稳定性而使用了Akima样条曲线。通过具有对位移和应力的行为约束的形状优化的数值例子来验证所提出的技术的性能。为了证明现有技术的有效性，将通过所提出的技术获得的结果与过去文献的其他技术进行比较。为了确保在形状优化期间可接受的溶液精度水平，对于初始问题几何形状的H-细化已经执行，因为在优化过程期间产生的一些形状可以具有低现场节点密度。

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