基于确定性及稳健性优化的水下航行器外形减阻研究

Research on drag reduction of underwater vehicle shape based on deterministic and robust optimization

  • 摘要: 【目的】为提高水下航行器的续航力和性能稳定性。【方法】选择三种常见回转体作为AUV主体外形的候选,先进行了三种模型的建立获得模型的体积并进行了水动力的分析获得模型的阻力,择优选择Myring线型作为AUV的主体外形。根据Myring线型方程确定线型参数的取值范围,采用拉丁超立方试验设计方法进行了设计变量的采样并求出样本点的响应值,以样本点为基础构建了近似模型,近似模型建立完成后进行拟合度分析、灵敏度分析以及响应曲面分析,近似模型拟合度达标后建立多目标优化模型进行了确定性优化,获得最优解并对最优解进行了可靠度分析,之后为提高优化解的可靠度展开了稳健性优化。【结果】确定性优化后阻力相对于初始外形阻力降低了5.96%,体积减小了0.78%;稳健性优化相对于确定性优化阻力提高了0.35%,体积增大了0.14%,可靠度达到6σ水平。【结论】确定性优化及稳健性优化均减小了水下航行器的阻力,稳健性优化提高了设计变量的稳定性。

     

    Abstract: Objectives In order to improve the endurance and performance stability of autonomous underwater vehicles. Methods Three common rotating bodies were selected as candidates for the main body shape of the AUV. Three models were first established to obtain the volume of the models, and hydrodynamic analysis was conducted to obtain the resistance of the models. The preferred choice was the Myring line type as the main body shape of the AUV. According to the Myring line equation, the range of line parameters was determined. The Latin hypercube experimental design method was used to sample the design variables and obtain the response values of the sample points. Based on the sample points, an approximate model was constructed. After the approximate model was established, fitting degree analysis, sensitivity analysis, and response surface analysis were carried out. After the fitting degree of the approximate model reached the standard, a multi-objective optimization model was established for deterministic optimization, and the optimal solution was obtained. The reliability analysis of the optimal solution was carried out, and then robustness optimization was carried out to improve the reliability of the optimized solution. Results After deterministic optimization, the resistance decreased by 5.96% compared to the initial external resistance, and the volume decreased by 0.78%; Compared to deterministic optimization, robust optimization has increased resistance by 0.35%, increased volume by 0.14%, and achieved a reliability level of 6 σ. Conclusions Deterministic optimization and robust optimization both reduce the resistance of underwater vehicles, while robust optimization improves the stability of design variables.

     

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