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A Fuzzy Logic Knowledge-Based Approach for Finite Element Mesh Generation and Analysis

机译:基于模糊逻辑知识的有限元网格生成与分析方法

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摘要

A fuzzy logic knowledge-based approach, FUZZYMESH, for finite element mesh generation and analysis is presented. The proposed approach initiates the adaptive process with a high quality initial mesh that is more refined around the critical points/regions in the problem domain. In order to create high quality initial meshes, the heuristic knowledge, past experience, common sense, and ad hoc methods of finite element specialists are incorporated into the knowledge base of the fuzzy system. Using the linguistic variable concept and approximate reasoning techniques, the fuzzy system makes expert decisions about the initial mesh design by considering the geometric information, as well as the boundary and loading conditions. The decision process includes the determination of priority of critical points/regions and the prediction of mesh sizes for them. According to the mesh size information, a near-optimal initial mesh is created with an automatic mesh generator that is based on the advancing front mesh generation technique. The performance of the proposed approach was measured and evaluated in terms of efficiency and accuracy. The evaluation included comparison between the results of a code based on the proposed fuzzy logic knowledge-based approach, FUZZYMESH, and the conventional approach, which starts the finite element analysis with different meshes, by solving various problems. The global as well as local errors of different solutions were examined and compared. The CPU times for different approaches to achieve a particular accuracy were also measured and compared. The results showed that due to better quality of initial meshes, FUZZYMESH results in lower levels and more accurate error estimates. In turn, the proposed approach is able to solve the problem with a more accurate solution at less cost.
机译:提出了一种基于模糊逻辑知识的有限元网格生成方法FUZZYMESH。所提出的方法以高质量的初始网格启动自适应过程,该网格在问题域中的关键点/区域周围得到了进一步完善。为了创建高质量的初始网格,将有限元专家的启发式知识,过去的经验,常识和即席方法结合到模糊系统的知识库中。使用语言变量概念和近似推理技术,模糊系统通过考虑几何信息以及边界和加载条件,对初始网格设计做出专家决策。决策过程包括确定关键点/区域的优先级以及预测关键点/区域的网格大小。根据网格大小信息,使用基于先进的前网格生成技术的自动网格生成器创建接近最佳的初始网格。在效率和准确性方面对提出的方法的性能进行了测量和评估。评估包括比较基于所提出的基于模糊逻辑知识的方法FUZZYMESH和常规方法(通过解决各种问题来开始使用不同网格进行有限元分析)的代码结果之间的比较。检查并比较了不同解决方案的全局和局部误差。还测量并比较了用于实现特定精度的不同方法的CPU时间。结果表明,由于初始网格的质量较高,FUZZYMESH导致较低的级别和更准确的误差估计。反过来,所提出的方法能够以更低成本解决更精确的问题。

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