Найдено научных статей и публикаций: 2, для научной тематики: Genetic Algorithms
1.
Rao, Jagu S. and Tiwari R.
- International Journal for Computational Methods in Engineering Science & Mechanics , 2010
Design optimization of axial hybrid magnetic thrust bearings (with bias magnets) is
carried out using multi-objective evolutionary algorithms (MOEAs) and compared with
the case of electro magnetic bearings (without bias magnets). Mathematical models of
objective functions and associated constraints ...
Design optimization of axial hybrid magnetic thrust bearings (with bias magnets) is
carried out using multi-objective evolutionary algorithms (MOEAs) and compared with
the case of electro magnetic bearings (without bias magnets). Mathematical models of
objective functions and associated constraints have been presented and discussed. The
different aspects of MOEA implemented have been discussed. It is observed that the size
of the bearing with bias magnets is considerably reduced as compared to the case of
without bias magnets, with the objective function as the minimization of weight for the
same operating conditions. Similarly, current densities get reduced drastically with
biased magnets when the objective function is chosen as the minimisation of the
powerloss. For illustration of various performances of the bearing, a typical design has
been chosen from the final optimized population by an 'a posteriori' approach.
Sensitivities for both the objective functions with respect to the outer radius, the inner
radius, and the height of coil are observed to be approximately in the ratio 2.5:1.6:1.
Analysis of final optimized population has been carried out and is compared with the
case of without bias magnets and some salient points are observed in the case of using
bias magnets.
2.
Rao, Jagu S. and Tiwari R.
- International Journal for Computational Methods in Engineering Science and Mechanics , 2008
An optimum design of thrust magnetic bearings has been carried
out using multi-objective genetic algorithms (MOGAs). The
power-loss and the weight have been selected as the minimization
type objective functions for the optimum design. The maximum
space available, the maximum current density that can...
An optimum design of thrust magnetic bearings has been carried
out using multi-objective genetic algorithms (MOGAs). The
power-loss and the weight have been selected as the minimization
type objective functions for the optimum design. The maximum
space available, the maximum current density that can be supplied
in the coil, the maximum magnetic flux density that is allowed in the
stator-iron (i.e., the magnetic flux density at the saturation), and
the load required to be supported have been chosen as constraints.
The inner and outer radii of the coil, and the height of the coil
have been proposed as design variables. Apart from the comparison
of performance parameters in the form of figures and tables,
designs are also compared through line diagrams. Post-processing
has been done on the final optimized population by studying the
variation of different parameters with respect to objective functions.
The saturation of magnetic flux density and the saturation of
coil current density are observed to be the salient points, where the
major changes occur in the behavior of different design parameters.
A criterion for the choice of one of the best design based on
the minimum normalized distance near the utopia point is used.
A sensitivity analysis has been done on a chosen design by giving
small perturbations on the design variables. It is observed that the
effect of the outer radius of the coil on the objective functions is
nearly double as compared to other two design variables.