期刊
JOURNAL OF MARINE SCIENCE AND ENGINEERING
卷 9, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/jmse9050478
关键词
robotic fish; optimal design; multi-objective optimization; multidisciplinary design optimization; computational fluid dynamics (CFD); artificial neural network; conceptual design
资金
- General Program of National Natural Science of China A study on the water absorption property of the buoyancy material for the full ocean depth manned submersible [51879157]
- Construction of a Leading Innovation Team project by the Hangzhou Municipal government
- Zhejiang Key RD Program [2021C03157]
- Westlake University [041030150118]
In this paper, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. By combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, the optimized robotic fish shows better performance than the initial design.
Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish's hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method's computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy's effectiveness.
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