基于知识挖掘的HDMR优化方法与工程应用

doi:10.3901/JME.2024.13.122

机械工程学报 ›› 2024, Vol. 60 ›› Issue (13): 122-129.doi: 10.3901/JME.2024.13.122

• 多学科仿真与优化设计 • 上一篇下一篇

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基于知识挖掘的HDMR优化方法与工程应用

刘肖佐¹, 王鹏¹, 何瑞轩¹, 李靖璐¹, 董华超¹, 温志文²

1. 西北工业大学航海学院西安 710072;
2. 西安光学精密机械研究所西安 710077

收稿日期:2023-10-18 修回日期:2024-03-20 出版日期:2024-07-05 发布日期:2024-08-24
作者简介:刘肖佐,男,1997年出生,博士研究生。主要研究方向为水下航行器优化设计。E-mail:liu_xz@mail.nwpu.edu.cn;王鹏(通信作者),男,1978年出生,教授,博士研究生导师。主要研究方向为水下航行器总体设计、多学科优化。E-mail:wangpeng305@nwpu.edu.cn
基金资助:
国家自然科学基金(52175251,52205268)和国防基础科研计划(JCKY0206B005)资助项目。

HDMR Optimization Method and Application Based on Knowledge Mining

LIU Xiaozuo¹, WANG Peng¹, HE Ruixuan¹, LI Jinglu¹, DONG Huachao¹, WEN Zhiwen²

1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072;
2. Xi'an Precision Machinery Research Institute, Xi'an 710077

Received:2023-10-18 Revised:2024-03-20 Online:2024-07-05 Published:2024-08-24

摘要/Abstract

摘要： 在工程问题中，尽管积累了大量的实验、仿真和设计经验，传统的设计方法仍然面临着知识利用率不高的挑战。为了有效利用知识信息来加速装备的设计与开发，一种基于知识挖掘的高维模型表示(High dimensional model representation,HDMR)优化方法被提出。首先，引入了一种改进的多元模型筛选策略，以提高HDMR子项的构建效率和预测精度。之后，提出了一种基于知识挖掘的优化策略。该策略使用全局代理模型替代真实函数，以最优样本为中心点构建HDMR子项，并在每个子项代表的维度上分别寻找局部最优点，并通过置信度对比挖掘全局潜在较优点，以加速算法寻优。最终，利用该方法开展翼身融合水下翔机(Blended-wing-body underwater glider, BWBUG)外形优化设计。在满足体积约束的条件下，将滑翔机的升阻比提升了5.04%，优于无知识辅助下升阻比提升2.93%的优化结果，验证了所提方法中知识挖掘的作用。

关键词: 知识挖掘, 高维模型表示, 全局优化, 翼身融合水下滑翔机

Abstract: Despite the wealth of experimental, simulation, and design experience in engineering, traditional design methods struggle with low knowledge utilization. To address this, a high dimensional model representation (HDMR) optimization method grounded in knowledge mining is presented. The approach employs an improved multivariate model screening strategy for enhanced efficiency and prediction accuracy of HDMR subcomponents. The optimization strategy integrates a global surrogate model, utilizing optimal samples to construct HDMR sub-items and identifying local advantages in each dimension. Confidence comparisons expedite global potential advantage discovery, accelerating algorithmic optimization. The proposed method is applied to shape optimization for a blended-wing-body underwater glider (BWBUG). Under volume constraints, the glider's lift-to-drag ratio increases by 5.04%, surpassing the 2.93% increase without knowledge assistance. This validates the impactful role of knowledge mining in the proposed methodology, providing a novel perspective and method for high-dimensional optimization problems while contributing to the advancement and application of optimization algorithms.

Key words: knowledge mining, high dimensional model representation (HDMR), global optimization, blended-wing-body underwater glider (BWBUG)

中图分类号:

TH156

刘肖佐, 王鹏, 何瑞轩, 李靖璐, 董华超, 温志文. 基于知识挖掘的HDMR优化方法与工程应用[J]. 机械工程学报, 2024, 60(13): 122-129.

LIU Xiaozuo, WANG Peng, HE Ruixuan, LI Jinglu, DONG Huachao, WEN Zhiwen. HDMR Optimization Method and Application Based on Knowledge Mining[J]. Journal of Mechanical Engineering, 2024, 60(13): 122-129.

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基于知识挖掘的HDMR优化方法与工程应用

HDMR Optimization Method and Application Based on Knowledge Mining

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