Shape optimisation of blended-wing-body underwater gliders based on free-form deformation

ABSTRACT

As a type of Autonomous Underwater Vehicles (AUV), blended-wing-body underwater gliders (BWBUGs) have drawn much attention due to their unique performance advantages in long-distance navigation missions. Because the configuration is the direct component to influence the navigation efficiency, it is significant to perform shape optimisation of BWBUGs. In this paper, an optimisation framework is presented, where Free-Form Deformation (FFD) is adopted for geometric parameterisation. Besides, an Euler-based CFD solver with a discrete adjoint method is applied for numerical simulation and gradients calculation, and Sequential Quadratic Programming (SQP) is employed as the optimiser. With the help of the proposed framework, an optimised BWBUG is regarded as the initial shape and four shape optimisation cases are carried out for different design purposes. All the objective functions aim to increase the lift-drag ratio under the thickness and volume constraints. The simulation results show that all the cases achieve a higher lift-drag ratio.

KEYWORDS:

• Blended-wing-body underwater glider
• shape optimisation
• optimisation framework
• free-form deformation

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Jinglu Li, the Ph.D. student in Northwestern Polytechnical University, Xi’an, China. His current research interests include design of underwater vehicle, CFD and shape optimisation of underwater vehicles.

Peng Wang, the professor and the vice dean of the graduate school in Northwestern Polytechnical University, Xi’an, China. His research interests include surrogate-based design optimisation, multidisciplinary design optimisation, multiple criteria decision making, and design of underwater vehicles.

Huachao Dong, received the Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China. His research interests include surrogate-based design optimisation, surrogate model and design of underwater vehicles.

Xumao Wu, received the master's degree from Northwestern Polytechnical University, Xi’an, China. His research interests include shape optimisation of underwater vehicles, CFD and deep learning.

Xu Chen, the Ph.D. student in Northwestern Polytechnical University, Xi’an, China. His research interests include multidisciplinary design optimisation and surrogate-based optimisation.

Caihua Chen, the Ph.D. student in Northwestern Polytechnical University, Xi’an, China. His research interests include intelligent optimisation algorithm, parallel computing and surrogate-based optimisation.

ORCID

Jinglu Li http://orcid.org/0000-0001-6397-5620

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant number 51875466, 51805436].