Using Metamodeling and Fluid-Structure Interaction Analysis in Multi-Objective Optimization of a Butterfly Valve

Laura Pałys, M. Mrzygłód
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引用次数: 1

Abstract

Along with the increase in computing power, new possibilities for the use of parametric coupled analysis of fluid flow machines and metamodeling for many branches of industry and medicine have appeared. In this paper, the use of a new methodology for multiobjective optimization of a butterfly valve with the application of the fluid-structure interaction metamodel is presented. The optimization objective functions were to increase the value of the KV valve’s flow coefficient while reducing the disk mass. Moreover, the equivalent von Mises stress was accepted as an additional constraint. The centred composite designs were used to plan the measuring point. Full second-order polynomials, non-parametric regression, Kriging metamodeling techniques were implemented. The optimization process was carried out using the multi-objectives genetic algorithm. For each metamodel, one of the optimization candidates was selected to verify its results. The best effect was obtained using the Kriging method. Optimization allowed to improve the KV value by 37.6%. The metamodeling process allows for the coupled analysis of the fluid flow machines in a shorter time, although its main application is geometry optimization.
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基于元建模和流固耦合分析的蝶阀多目标优化
随着计算能力的提高,在工业和医学的许多分支中使用流体流动机器的参数耦合分析和元建模的新可能性已经出现。本文提出了一种基于流固耦合元模型的蝶阀多目标优化方法。优化的目标函数是在减小阀瓣质量的同时提高KV阀的流量系数。此外,等效冯米塞斯应力被接受为一个额外的约束。采用中心复合设计来规划测点。实现了全二阶多项式、非参数回归、Kriging元建模技术。优化过程采用多目标遗传算法进行。对于每个元模型,选择一个优化候选者来验证其结果。采用克里格法获得最佳效果。优化后的KV值提高了37.6%。元建模过程允许在较短的时间内对流体流动机器进行耦合分析,尽管它的主要应用是几何优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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