Modifed Multifdelity Surrogate Model Based on Radial Basis Function with Adaptive Scale Factor

Multifdelity surrogates(MFSs)replace computationally intensive models by synergistically combining information from diferent fdelity data with a signifcant improvement in modeling efciency.In this paper,a modifed MFS(MMFS)model based on a radial basis function(RBF)is proposed,in which two fdelities of information can be analyzed by adaptively obtaining the scale factor.In the MMFS,an RBF was employed to establish the low-fdelity model.The correlation matrix of the high-fdelity samples and corresponding low-fdelity responses were integrated into an expansion matrix to determine the scaling function parameters.The shape parameters of the basis function were optimized by minimizing the leave-one-out cross-validation error of the high-fdelity sample points.The performance of the MMFS was compared with those of other MFS models(MFS-RBF and cooperative RBF)and single-fdelity RBF using four benchmark test functions,by which the impacts of diferent high-fdelity sample sizes on the prediction accuracy were also analyzed.The sensitivity of the MMFS model to the randomness of the design of experiments(DoE)was investigated by repeating sampling plans with 20 diferent DoEs.Stress analysis of the steel plate is presented to highlight the prediction ability of the proposed MMFS model.This research proposes a new multifdelity modeling method that can fully use two fdelity sample sets,rapidly calculate model parameters,and exhibit good prediction accuracy and robustness.

Multidisciplinary co-design optimization of structural and control parameters for bucket wheel reclaimer

Bucket wheel reclaimer(BWR)is an extremely complex engineering machine that involves multiple disciplines,such as structure,dynamics,and electromechanics.The conventional design strategy,namely,sequential strategy,is structural design followed by control optimization.However,the global optimal solution is difficult to achieve because of the discoordination of structural and control parameters.The co-design strategy is explored to address the aforementioned problem by combining the structural and control system design based on simultaneous dynamic optimization approach.The radial basis function model is applied for the planning of the rotation speed considering the relationships of subsystems to minimize the energy consumption per volume.Co-design strategy is implemented to resolve the optimization problem,and numerical results are compared with those of sequential strategy.The dynamic response of the BWR is also analyzed with different optimization strategies to evaluate the advantages of the strategies.Results indicate that co-design strategy not only can reduce the energy consumption of the BWR but also can achieve a smaller vibration amplitude than the sequential strategy.