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Hull optimization of an underwater vehicle based on dynamic surrogate model

机译:基于动态代理模型的水下车辆优化

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A parallel multidisciplinary optimization design is proposed for the lines design of an underwater vehicle. Resistance and energy consumption are concerned about in obtaining the optimized lines. Collaborative optimization strategy is adopted to construct the optimization framework. The simulated annealing algorithm in combination of the modified method of feasible directions are taken as the optimization algorithms. CFD based simulation is conducted to analyze the resistance of the underwater vehicle. To improve the optimization efficiency, a surrogate model is incorporated into the optimization framework to approximate the CFD calculation. Unlike the conventional static surrogate models used in the design of underwater vehicles, a dynamic surrogate based on trust region and lower confidence bound is proposed. An adaptive balance parameter is taken in the lower confidence bound approach to guarantee the tradeoff between the exploitation and exploration ability of the dynamic surrogate model. Optimal Latin hypercube algorithm is employed as the method of design of experiments. Radial basis function is employed to construct the surrogate model. SUBOFF model is used to demonstrate the proposed optimization scheme. Lines of the underwater vehicle are determined and optimization results of the resistance and energy consumption show the effectiveness of optimization scheme.
机译:提出了一种用于水下车辆的线路设计的平行多学科优化设计。抵抗和能量消耗涉及获得优化的线路。采用协作优化策略来构建优化框架。仿形退火算法与改进的可行方法的组合算法作为优化算法。进行基于CFD的仿真以分析水下车辆的电阻。为了提高优化效率,将代理模型纳入优化框架以近似CFD计算。与在水下车辆设计中使用的传统静替代模型不同,提出了一种基于信任区域和较低置信度的动态代理。采用自适应平衡参数,以较低的置信方法,以保证动态代理模型的开发与勘探能力之间的权衡。采用最佳拉丁杂交算法作为实验设计方法。采用径向基函数来构建替代模型。超级模型用于展示所提出的优化方案。确定水下车辆的线路确定,电阻和能量消耗的优化结果显示了优化方案的有效性。

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