Construction of an adaptive sampling surrogate model and application in composite material structure optimization

An adaptive surrogate model was proposed using an adaptive sampling and enhanced radial basis function(ERBF). The adaptive sampling method was used to determine the appropriate number of sample points and to improve the adaptive capacity of the surrogate model. New sampling points were located in sparse areas and ensure that all sample points were evenly distributed in the design space to improve the accuracy of the surrogate model. The standard error was used to determine the accuracy of the surrogate model and to determine whether the surrogate model was updated. A conditional random sampling was used to compare the adaptive sampling methods in this paper. It is found that the accuracy of the surrogate model with adaptive sampling method is higher than that of the conditional random sampling method. This adaptive surrogate model is combined with the multi-island genetic algorithm to optimize the fiber angle of carbon fiber reinforced epoxy resin composites for the rotor-arm and obtain the highest first-order modal value of the rotor-arm. The optimization results show that the fiber angle of carbon fiber reinforced epoxy resin composites has a great influence on the first-order modal value of the rotor-arm. The optimization result obtains the optimum layer angles, and the first-order modal value of the rotor-arm is kept away from the rotation frequency to prevent resonance.