The dynamic characteristics of the cavity mirror support structure strongly influence the quality of the output beam. However, the contradiction between excellent dynamic performance and light weight make the design process challenging. To cope with the problems encountered in the original design of a chemical oxygen iodine laser system, this paper presents a two-dimensional adjustable support structure based on spherical constraints with large specific stiffness in the initial design phase. Subsequently, a two-level optimization strategy containing a macro design and a detailed design is adopted to optimize the initial structure. At the macro design stage, a two-step topology optimization procedure is introduced, in which the scale of the optimization model is dramatically reduced using the independent continuous mapping algorithm to improve the calculation speed in the first step, and the gray elements are eliminated using the bi-directional evolutionary structural optimization method to clearly obtain the optimal topology in the second step. This method is verified to overcome the defect of low efficiency, while still eliminating gray elements. At the detailed design stage, an adaptive surrogate model and the multi-objective design optimization method are employed to seek the best compromise between the lower weight and higher dynamic performance. The results from the application to the example of the cavity mirror support structure show that the mass is reduced by 41.8%, and the dynamic performance requirement is fulfilled.
This paper presents a novel approach of principal component analysis- (PCA-) assisted back propagation (BP) neural networks for the problem of rotor blade load prediction. 86.5 hours of real flight data were collected from many steady-state and transient flight maneuvers at different altitudes and airspeeds. Prediction of the blade loads was determined by the PCA-BP model from 16 flight parameters measured and monitored by the flight control computer already present in the helicopter. PCA was applied to reduce the dimension of the flight parameters influencing the component load and eliminate the correlation among flight parameters. Thus, obtained principal components were used as input vectors of the BP neural network. The combined PCA-BP neural network model was trained and tested by real flight data. Comparison of this model and to a BP neural network model as well as to a multiple linear regression (MLR) model was also done. The results of comparison demonstrate that the PCA-BP model has higher prediction precision with an average error of 2.46%, while 4.49% for BP and 10.20% for MLR. The results also reveal that the PCA-BP model has a shorter convergence path than the BP model. This method not only is useful in establishing the load spectra of helicopter rotor in-service where installation of strain gauges is impractical but also can reduce the cost of installation and maintenance measured by strain gauges.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.