An Efficient Surrogate-Based Optimization Method for BWBUG Based on Multifidelity Model and Geometric Constraint Gradients
An Efficient Surrogate-Based Optimization Method for BWBUG Based on Multifidelity Model and Geometric Constraint Gradients
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基于多重保真模型和几何约束梯度的 BWBUG 高效代理优化方法
DOI：
10.1155/2021/6939863
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发表时间：
2021
影响因子：
--
通讯作者：
Zhu Xinyao
中科院分区：
文献类型：
--
作者：
Zhang Daiyu;Zhang Bei;Wang Zhidong;Zhu Xinyao
对翼身融合水下滑翔器进行外形优化可以显著提高其滑翔性能。然而，传统的基于代理的优化方法中的高保真CFD分析和几何约束计算是昂贵的。提出了一种基于多保真度模型和几何约束梯度信息的高效代理优化方法。通过建立形状参数化模型、推导几何约束梯度解析表达式、构造多保真度代理模型，减少了BWBUG形状优化过程中高保真CFD模型和几何约束的计算次数，大大提高了优化效率。最后，通过对BWBUG的形状优化，并与传统的基于代理的优化方法进行比较，验证了该方法的有效性和效率。
Performing shape optimization of blended-wing-body underwater glider (BWBUG) can significantly improve its gliding performance. However, high-fidelity CFD analysis and geometric constraint calculation in traditional surrogate-based optimization methods are expensive. An efficient surrogate-based optimization method based on the multifidelity model and geometric constraint gradient information is proposed. By establishing a shape parameterized model, deriving analytical expression of geometric constraint gradient, constructing multifidelity surrogate model, the calculation times of high-fidelity CFD model and geometric constraints are reduced during the shape optimization process of BWBUG, which greatly improve the optimization efficiency. Finally, the effectiveness and efficiency of the proposed method are verified by performing the shape optimization of a BWBUG and comparing with traditional surrogate-based optimization methods.