Quadrilateral mesh smoothing using a genetic algorithm

This dissertation presents the results of an investigation in which genetic algorithms were used for general 2-D quadrilateral mesh smoothing, using one or multiple nodes, in finite element analysis (FEA). Specifically, a useful engineering tool was developed for achieving 2-D quadrilateral mesh smoothing. The tool is called the genetic algorithm smoother (GAS). To improve the operation of GAS, a technique termed feasible circles was developed to reduce the size of the requisite search areas, thereby dramatically improving genetic algorithm performance. GAS can be used to achieve either pseudo-meshing (predefined connectivity) or mesh untangling of up to 25 elements (16 nodes). In addition, GAS can simultaneously manipulate up to 64 nodes, with increasingly long computer run times as the problem size increases.; Five variations of GAS (GAS-1, GAS-4, GAS-9, GAS-N, and GAS-M) are presented with the associated results of using a variety of FEA models. The results of these experiments are compared to results achieved using the Laplace smoothing technique. These results indicate that GAS is a reasonably effective tool for solving the problem of 2-D mesh smoothing.; GAS requires that a measure of solution quality be defined by the user. In this effort, a distortion metric was used to quantify the “goodness” of individual quadrilateral elements. The distortion metric was combined with weighted measurements of the interior angles and the aspect ratio of elements to form an objective function for GAS. Other implementation details, such as the convergence criteria, population size, crossover probability, and mutation rate, are presented in the dissertation.; Parametric studies designed to test the effectiveness of GAS were performed, and the results are presented in both tabular and graphical form. Optimal genetic algorithm parameters are listed for the mesh-smoothing process using a steady-state genetic algorithm. Planned future work is outlined and possible genetic algorithm smoothing applications are discussed.