Causal-relationship-assisted shape design optimization for blended-wing-body underwater gliders

Abstract

Blended-wing-body underwater gliders (BWBUGs) are a new generation of underwater vehicles that have been successfully applied to long-range missions. It is generally recognized that shapes with a high aspect ratio have a higher lift–drag ratio (LDR) and superior fluid performance. However, the pursuit of higher LDR cannot achieve the optimal overall performance, and there are complex design factors. It is necessary but difficult to conduct a thorough examination of shape layout, energy-carrying and other issues. In this article, the layout and energy models are established, and the causal graph is used as a multidisciplinary analysis method to show the interaction between system variables. A shape design method based on surrogate models and causal graphs is proposed that incorporates knowledge into optimization by combing the information flow and finding the relationship between variables. The results show that causal-assisted optimization can mine the characteristics of variables and discover more valuable shape designs.

KEYWORDS:

• Multidisciplinary design
• causal graph
• global optimization
• blended-wing-body underwater gliders

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Funding

This work is partially sponsored by the National Natural Science Foundation of China (Grant No. 52175251, 52205268), the National Basic Scientific Research Program of China (Grant No. JCKY2021206B005).