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An integration of a low cost adaptive remeshing strategy in the solution of structural shape optimization problems using evolutionary methods

机译:使用进化方法将低成本自适应重划策略集成到结构形状优化问题的解决方案中

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摘要

Evolutionary methods are a powerful and robust tool for the solution of structural shape optimization problems. Nevertheless, the use of these methods requires the structural analysis of an important number of different designs, this making the computational cost of the analysis of each design a critical issue. For this reason, each design must be analyzed at a minimum computational cost but ensuring a minimum quality of the results. It is well known that the cheapest mesh for producing a solution with a fixed quality at minimum cost is an adapted one. Nevertheless, traditional adapted meshes are obtained from adaptive remeshing strategies, where each design has to be analyzed more than once, thereby also causing a high computational cost. This work presents a new strategy that allows generating an adapted mesh for each design without the necessity of performing a full adaptive remeshing procedure for each of them. It is based on the use of sensitivity analysis of all magnitudes related with adaptive remeshing (location of nodes, error estimation, etc.) with respect to the design variables. This sensitivity analysis is performed only once using a geometry of reference and it is used to project the results of the corresponding analysis to all other designs to be analyzed. The projected information allows generating an appropriate adapted mesh for each new design in one shot, greatly reducing the computational cost compared with standard strategies.
机译:进化方法是解决结构形状优化问题的强大而强大的工具。但是,使用这些方法需要对大量不同设计进行结构分析,这使得每个设计分析的计算成本成为关键问题。因此,必须以最小的计算成本来分析每个设计,但要确保结果的最低质量。众所周知,用于以最小的成本生产具有固定质量的解决方案的最便宜的筛网是一种适合的筛网。然而,传统的自适应网格是从自适应网格划分策略中获得的,其中每个设计必须进行多次分析,从而也导致了高昂的计算成本。这项工作提出了一种新的策略,该策略允许为每个设计生成一个自适应的网格,而无需为每个设计执行完整的自适应重新网格化过程。它基于对所有与设计变量有关的自适应重新网格化(节点的位置,误差估计等)的幅度的敏感性分析。使用参考几何图形仅执行一次此灵敏度分析,并将其用于将相应分析的结果投影到所有其他要分析的设计。投影的信息可以一次性生成针对每个新设计的合适的网格,与标准策略相比,大大降低了计算成本。

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