Design optimization is an iterative process that requires long, arduous and costly labor to perform. OPTSTAR, powered by COSMOS/FFE, gives you a computerized design optimization and sensitivity analysis capability which makes it possible to maximize the product performance and minimize its material cost - effortlessly, economically and quickly. 

To optimize a design, you first decide on the parameters that can be changed in order to reach an optimum solution. These parameters are called design variables. Changes in the variables are made within the specified range to achieve the design objective, such as minimizing the weight of the product. Therefore, the objective function must be defined. While changing the design variables to minimize or maximize the objective function, it is also important to keep sight of the system response in terms of stresses, frequencies or other relevant quantities. These quantities are known as constraints and must be maintained within the specified levels to achieve an acceptable optimum design. 


The basic features of OPTSTAR are: 

Support of both size and shape optimization. 
Selection from a wide range of options to specify the objective function. Possible objective functions are: Volume, weight, stress, strain, displacement, natural frequency, buckling load factor, fatigue usage factor, temperature, heat flux, and temperature gradient. 
Behavior constraints can be stresses, strains, displacements, frequencies, buckling loads, temperatures, heat fluxes, temperature gradients, or a combination of them. 
The use of sensitivity analysis to examine the effect of various design variables on the results of the optimization analysis. 
Global sensitivity, where all design variables are allowed to change between their lower and upper boundaries. 
Local sensitivity, in which only one variable is perturbed at a time while all others remain unchanged. 
Offset sensitivity, where the values of a series of design variables are specified based on which the desired analysis is performed. 
Support of multi-disciplinary optimization analysis involving results from static, dynamic, thermal, fatigue, nonlinear, and buckling analyses. It is also possible to include the effect of multiple load cases for more realistic optimization studies. 
User-defined objective function and behavior constraints for optimization analysis. 
User-defined response quantities for sensitivity analysis.