arXiv subjects

Gary F. Dargush

Publications and source records attributed to Gary F. Dargush.

20 records · Page 2Linked to original sources

arXiv2010

Analysis of bi-material interface cracks with complex weighting functions and non-standard quadrature

A boundary element formulation is developed to determine the complex stress intensity factors associated with cracks on the interface between dissimilar materials. This represents an extension of the methodology developed previously by the authors for determination of free-edge generalized stress intensity factors on bi-material interfaces, which employs displacements and weighted tractions as primary variables. However, in the present work, the characteristic oscillating stress singularity is addressed through the introduction of complex weighting functions for both displacements and tractions, along with corresponding non-standard numerical quadrature formulas. As a result, this boundary-only approach provides extremely accurate mesh-independent solutions for a range of two-dimensional interface crack problems. A number of computational examples are considered to assess the performance of the method in comparison with analytical solutions and previous work on the subject. As a final application, the method is applied to study the scaling behavior of epoxy-metal butt joints.

physics.comp-ph↗

arXiv2007

A Compact Self-organizing Cellular Automata-based Genetic Algorithm

A Genetic Algorithm (GA) is proposed in which each member of the population can change schemata only with its neighbors according to a rule. The rule methodology and the neighborhood structure employ elements from the Cellular Automata (CA) strategies. Each member of the GA population is assigned to a cell and crossover takes place only between adjacent cells, according to the predefined rule. Although combinations of CA and GA approaches have appeared previously, here we rely on the inherent self-organizing features of CA, rather than on parallelism. This conceptual shift directs us toward the evolution of compact populations containing only a handful of members. We find that the resulting algorithm can search the design space more efficiently than traditional GA strategies due to its ability to exploit mutations within this compact self-organizing population. Consequently, premature convergence is avoided and the final results often are more accurate. In order to reinforce the superior mutation capability, a re-initialization strategy also is implemented. Ten test functions and two benchmark structural engineering truss design problems are examined in order to demonstrate the performance of the method.

cs.NE↗