Lines optimisation of an underwater vehicle using SMOTE and adaptive minimise LCB based dynamic surrogate models

ABSTRACT

The lines optimisation of an underwater vehicle based on a dynamic surrogate model is studied. Four performances including rapidity, manoeuverability, energy consumption and structure of the underwater vehicle are considered in the optimisation framework constructed by a generalised collaborative optimisation method. Expert knowledge based analytic hierarchy process is conducted to obtain the optimisation object that involves the four performances. Numerical simulation is performed to accurately analyze the rapidity, manoeuverability and structure performances of the underwater vehicle. To reduce the calculation burden, dynamic surrogate models are proposed to replace numerical simulation in the optimisation framework. To guarantee the optimisation efficiency and accuracy, a synthetic minority oversampling technique (SMOTE) and adaptive minimise lower confidence bound (LCB) are combined in constructing the dynamic surrogate models. The proposed optimisation strategy is applied to the SUBOFF model and compared with other dynamic surrogate models. Comparison results prove the advantages of the proposed dynamic surrogate model.

KEYWORDS:

• MDO
• dynamic surrogate model
• analytic hierarchy process
• lower confidence bound
• synthetic minority oversampling technique

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Fuzhou Institute of Oceanography: [Grant Number 2022F13].

Notes on contributors

Weifeng Pan

Weifeng Pan received the B.S. degree in Mechanical Manufacturing Process and Equipment from Fuzhou University, China, in 2020. He is currently pursuing the master's degree in Mechanical Engineering with Fuzhou University. His research interests include multidisciplinary design optimisation and underwater robotics.

Weilin Luo

Weilin Luo received the BS degree in Mechanical Manufacturing Process and Equipment from Jiaozuo Institute of Technology, China, in 1995, the MS degree in the Solid Mechanics from Fuzhou University, China, in 2003, the Ph.D. degree in Design and Manufacture of Naval Architecture and Ocean Structure from Shanghai Jiao Tong University (SJTU), China, in 2009. From July 2012 to July 2013, he worked as a postdoctoral fellow at the University of Lisbon, Portugal. From March 2016, he visited the University of California, Berkeley (UC, Berkeley) as a research scholar. Since 2017, he has been a Professor in the College of Mechanical Engineering and Automation, Fuzhou University, China. His research interests include ship manoeuvering and control, underwater robotics, and artificial intelligent techniques.