Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/5/478 |
| _version_ | 1797535816265760768 |
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| author | Hao Chen Weikun Li Weicheng Cui Ping Yang Linke Chen |
| author_facet | Hao Chen Weikun Li Weicheng Cui Ping Yang Linke Chen |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:50:42Z |
| format | Article |
| id | doaj.art-fab3659bcbfa47a49a6452b4745256e2 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T11:50:42Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-fab3659bcbfa47a49a6452b4745256e22023-11-21T17:47:09ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-04-019547810.3390/jmse9050478Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish SystemHao Chen0Weikun Li1Weicheng Cui2Ping Yang3Linke Chen4Zhejiang University-Westlake University Joint Training, Zhejiang University, Hangzhou 310024, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou 310024, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou 310024, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou 310024, ChinaKey Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou 310024, ChinaBiomimetic 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.https://www.mdpi.com/2077-1312/9/5/478robotic fishoptimal designmulti-objective optimizationmultidisciplinary design optimizationcomputational fluid dynamics (CFD)artificial neural network |
| spellingShingle | Hao Chen Weikun Li Weicheng Cui Ping Yang Linke Chen Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System Journal of Marine Science and Engineering robotic fish optimal design multi-objective optimization multidisciplinary design optimization computational fluid dynamics (CFD) artificial neural network |
| title | Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System |
| title_full | Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System |
| title_fullStr | Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System |
| title_full_unstemmed | Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System |
| title_short | Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System |
| title_sort | multi objective multidisciplinary design optimization of a robotic fish system |
| topic | robotic fish optimal design multi-objective optimization multidisciplinary design optimization computational fluid dynamics (CFD) artificial neural network |
| url | https://www.mdpi.com/2077-1312/9/5/478 |
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