Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm
The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
You, Xinxing [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: P-602 - The attitudes of students of high schools in Gjilan related to drug abuse - 2012, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:100 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.apor.2020.102148 |
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ELV050720279 |
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| 520 | |a The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. | ||
| 520 | |a The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. | ||
| 700 | 1 | |a Hu, Fuxiang |4 oth | |
| 700 | 1 | |a Dong, Shuchuang |4 oth | |
| 700 | 1 | |a Takahashi, Yuki |4 oth | |
| 700 | 1 | |a Shiode, Daisuke |4 oth | |
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10.1016/j.apor.2020.102148 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001044.pica (DE-627)ELV050720279 (ELSEVIER)S0141-1187(20)30102-4 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 33.00 bkl You, Xinxing verfasserin aut Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. Hu, Fuxiang oth Dong, Shuchuang oth Takahashi, Yuki oth Shiode, Daisuke oth Enthalten in Elsevier Science P-602 - The attitudes of students of high schools in Gjilan related to drug abuse 2012 Amsterdam [u.a.] (DE-627)ELV011183217 volume:100 year:2020 pages:0 https://doi.org/10.1016/j.apor.2020.102148 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 100 2020 0 |
| spelling |
10.1016/j.apor.2020.102148 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001044.pica (DE-627)ELV050720279 (ELSEVIER)S0141-1187(20)30102-4 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 33.00 bkl You, Xinxing verfasserin aut Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. Hu, Fuxiang oth Dong, Shuchuang oth Takahashi, Yuki oth Shiode, Daisuke oth Enthalten in Elsevier Science P-602 - The attitudes of students of high schools in Gjilan related to drug abuse 2012 Amsterdam [u.a.] (DE-627)ELV011183217 volume:100 year:2020 pages:0 https://doi.org/10.1016/j.apor.2020.102148 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 100 2020 0 |
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10.1016/j.apor.2020.102148 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001044.pica (DE-627)ELV050720279 (ELSEVIER)S0141-1187(20)30102-4 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 33.00 bkl You, Xinxing verfasserin aut Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. Hu, Fuxiang oth Dong, Shuchuang oth Takahashi, Yuki oth Shiode, Daisuke oth Enthalten in Elsevier Science P-602 - The attitudes of students of high schools in Gjilan related to drug abuse 2012 Amsterdam [u.a.] (DE-627)ELV011183217 volume:100 year:2020 pages:0 https://doi.org/10.1016/j.apor.2020.102148 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 100 2020 0 |
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10.1016/j.apor.2020.102148 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001044.pica (DE-627)ELV050720279 (ELSEVIER)S0141-1187(20)30102-4 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 33.00 bkl You, Xinxing verfasserin aut Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. Hu, Fuxiang oth Dong, Shuchuang oth Takahashi, Yuki oth Shiode, Daisuke oth Enthalten in Elsevier Science P-602 - The attitudes of students of high schools in Gjilan related to drug abuse 2012 Amsterdam [u.a.] (DE-627)ELV011183217 volume:100 year:2020 pages:0 https://doi.org/10.1016/j.apor.2020.102148 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 100 2020 0 |
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10.1016/j.apor.2020.102148 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001044.pica (DE-627)ELV050720279 (ELSEVIER)S0141-1187(20)30102-4 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 33.00 bkl You, Xinxing verfasserin aut Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. Hu, Fuxiang oth Dong, Shuchuang oth Takahashi, Yuki oth Shiode, Daisuke oth Enthalten in Elsevier Science P-602 - The attitudes of students of high schools in Gjilan related to drug abuse 2012 Amsterdam [u.a.] (DE-627)ELV011183217 volume:100 year:2020 pages:0 https://doi.org/10.1016/j.apor.2020.102148 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 100 2020 0 |
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Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. 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The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. 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shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm |
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Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm |
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The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. |
| abstractGer |
The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. |
| abstract_unstemmed |
The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bézier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20°. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling. |
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The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-to-drag ratio before the attack angle of 30° as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25° as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. 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