Multi-objective optimization and driving mechanism design for controllable wings of underwater gliders

Recently, underwater glider has become an effective tool for long-term ocean exploration. Controllable wing technology, which can improve comprehensive performance of underwater glider, has received widespread attention. This paper mainly proposes a multi-objective optimization based design method of controllable wings for underwater gliders. First, controllable wing configuration parameters are determined, specifically including wing span and sweep angle here. Then, hydrodynamic equations, containing controllable wing configuration parameters, are obtained by CFD simulation and quartic polynomial fitting. On this basis, the glider performance analysis model, quantitatively evaluating voyage velocity, static stability and energy consumption, are derived, and multi-objective optimization model of wing configuration parameters is established. Meanwhile, the relationship between controllable wing configuration parameters and the glider comprehensive performance is discussed. Especially, surrogate model technology is introduced into optimization calculation process to improve optimization efficiency. And non-dominated sorting genetic algorithm II is employed to obtain Pareto optimal set, which will make design objectives of driving mechanism of controllable wings clearer. Based on multiobjective optimization results and linkage synthesis method, a novel driving mechanism of controllable wings is designed, and corresponding principle prototype is manufactured. Besides, the motion characteristic, sealing and locking methods of the design scheme are also discussed.

Automated summary

This paper proposes a multi-objective optimization based design method of controllable wings for underwater gliders, which improves the performance of the glider. The optimization model is established by determining the wing configuration parameters and analyzing the glider performance. A novel driving mechanism of controllable wings is designed based on the optimization results.