Optimization design of antenna cover of deeply submerged body based onparallel variable-fidelity LCB algorithm

Abstract

ObjectivesIn order to reduce the weight while maintaining the performance of the antenna cover of a deeply submerged body, a parallel variable-fidelity lower confidence bound (PVF-LCB) approach is proposed to optimize the structure of the antenna cover.MethodsThe proposed algorithm adaptively allocates computational resources of different fidelities through a variable-fidelity LCB (VF-LCB) function, allowing it to select several candidate samples based on influence functions (IFs) constructed by the variable-fidelity Kriging model. Moreover, the proposed method is assisted with widely used constraint-handling methods to solve the structural optimization problem.ResultsThe proposed approach obtains an optimized antenna cover structure which satisfies all constraints. Compared with the well-known variable-fidelity optimization method, the optimized structure is about 50% lower in weight. Additionally, the proposed approach reduces the weight of the antenna cover by about 30% compared with the results of single-fidelity parallel optimization methods.ConclusionsThe proposed method can not only reduce the design cycle of engineering optimization, but improve the quality of the optimal solution, giving it certain development prospects and guiding significance for engineering applications.

Keywords

• variable-fidelity surrogate model
• lcb function
• influence function
• antenna cover of deeply submerged body
• structural optimization