The gating and riser system design is essential for both quality and cost in large-scale casting and is expected to reach several objectives simultaneously. However, even with the help of commercial simulation software, the design of gating and riser systems is still the result of a long-term trial-and-error optimal process owing to the conflict between the objectives. Several evolutionary algorithms (EAs) have been reported to be helpful in the selection of the geometrical dimensions of gating and riser systems. In this study, a route with sequential use of a multi-objective EA and single-objective optimization algorithm was developed to help design gating and riser systems, respectively. This route was applied in an actual case and verified using commercial simulation software. The results showed a dramatic decrease in the time cost in design and acceptable casting quality. Thus, the proposed design method is time-saving.
Due to the complex hysteresis phenomenon at a high angle of attack (AOA), modeling of unsteady aerodynamic coefficients usually encounters the problem that the parameter vector is too long and the simulation accuracy is not high. The article proposes an improved state-space model based on aerodynamics, applying Fourier analysis and the principal component analysis for model optimization. The likelihood criterion and GOIPSO (Iterative Particle Swarm Optimization Based on Genetic Operator) algorithm are established under the Gaussian assumption. The iterative PSO, into which the genetic algorithm's operators are integrated to calculate the optimization of the likelihood function, greatly reduced the probability of local optimization. Experiments show that the algorithm and model proposed in this paper greatly improves the model-fitting accuracy.
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