Grzegorz Dziatkiewicz

Immune identification of piezoelectric material constants using BEM

This article deals with an application of the artificial immune system (AIS) to the identification problem of piezoelectric structures analysed by the boundary element method (BEM). The AIS is applied to identify material properties of piezoelectrics. The AIS is a computational adaptive system inspired by the principles, processes and mechanisms of biological immune systems. The algorithms typically use the characteristics of immune systems, such as learning and memory to simulate and solve a problem in a computational manner. The main advantage of the AIS, contrary to gradient methods of optimization, is the fact that it does not need any information about the gradient of fitness function.

Comparison between PSO and AIS on the Basis of Identification of Material Constants in Piezoelectrics

The paper deals with an application of the artificial immune system (AIS) and particle swarm optimizer (PSO) to the identification problem of piezoelectric structures analyzed by the boundary element method (BEM). The AIS and PSO is applied to identify material properties of piezoelectrics. The AIS is a computational adaptive system inspired by the principles, processes and mechanisms of biological immune systems. The algorithms typically use the characteristics of the immune systems like learning and memory to simulate and solve a problem in a computational manner. The PSO algorithm is based on the models of the animals social behaviours: moving and living in the groups. PSO algorithm realizes directed motion of the particles in n-dimensional space to search for solution for n-variable optimisation problem.The main advantage of the bioinspired methods (AIS and PSO), contrary to gradient methods of optimization, is the fact that it does not need any information about the gradient of fitness function.

Advanced Continuum-Atomistic Model of Materials Based on Coupled Boundary Element and Molecular Approaches

The paper contains a description of a multiscale algorithm based on the boundary element method (BEM), coupled with a discrete atomistic model. The discrete model uses empirical pair-wise potentials and the Embedded Atom Method (EAM) to compute interaction forces between atoms. The Newton—Raphson method with the backtracking algorithm is applied to solve a nonlinear system of equations of the nanoscale model. The continuum domain is modeled using BEM with subregions. Some numerical results of simulations at the nanoscale are shown to examine the presented technique.

Artificial immune system for effective properties optimization of magnetoelectric composites

The optimization problem of the effective properties for magnetoelectric composites is considered. The effective properties are determined by the semi-analytical Mori-Tanaka approach. The generalized Eshelby tensor components are calculated numerically by using the Gauss quadrature method for the integral representation of the inclusion problem. The linear magnetoelectric constitutive equation is used. The effect of orientation of the electromagnetic materials components is taken into account. The optimization problem of the design is formulated and the artificial immune system is applied to solve it.