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In this paper, a multi purpose function is presented for optimum design in circular hydrostatic axial bearings. One or more parameters that are directly or indirectly dependent on given data are optimized. Usually in the local optimization methods, it is not possible to optimize several parameters simultaneously. In this research, simultaneous optimization of the power loss and the oil temperature rise as effective parameters for increase in efficiency of bearing is considered. The genetic algorithm is used in simultaneous optimization of the power and the temperature values. The accuracy of the multi purpose optimization is evaluated by a practical sample and the obtained results of simultaneous combination of parameters effect are compared with distinct functions.

In present paper, the inverse problem of cavity identification, i.e. determining the location and geometric shape of a cavity inside a two dimensional elastic body using temperature measurements obtained by performing a simple heat conduction test is investigated. The Boundary Element Method (BEM) coupled with Genetic Algorithm (GA) and Conjugate Gradient Method (CGM) is used to solve this inverse problem. A fitness function which is the squared differences between measured temperatures and calculated ones at the same locations on the exterior boundary of a sample containing a cavity is minimized using CGM. Considering that inverse problems are generally ill-posed and local optimization methods require a good initial guess, a mete heuristic model based on GA for determining a good initial guess of the cavity is presented in this study. Then this initial guess is used by CGM to achieve accurate location and geometric shape of cavity. The major cases application of this research is in the casting industry.

The effect of boundary conditions on the solution of the inverse problem of identifying the geometry and location of a cavity inside an elastic solid body using displacement measurements obtained from a tension test is investigated. The boundary elements method (BEM) coupled with the genetic algorithm (GA) and the conjugate gradient method (CGM) are implemented in this identification problem. A fitness function which is defined as the squared differences between the computed and measured displacements is minimized. The best initial guess of the unknown shape and location of the cavity is found by the GA, then this initial guess is used by the CGM to achieve convergence. The imposed boundary conditions, i.e. geometrical constrain and specified tractions are kept constant during all iterations. Certainly changes in the boundary conditions can be effective in the correct identification of the shape and location of the cavity. In this study the effect of different boundary conditions on the convergence is investigated and the best and the most suitable boundary conditions which results in the faster and more accurate convergence are found.