Layout optimization for underwater nozzle array of air-lifted artificial upwelling system based on discrete particle swarm algorithm
•Modeling artificial upwelling efficiency under crossflow.•Optimized nozzle layout improves upwelling efficiency.•Discrete particle swarm algorithm enhances carbon sequestration.•Increasing nozzles will improve performance, but raises investment cost.•Reference for engineering design of artificial u...
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| Vydané v: | Applied ocean research Ročník 140; s. 103724 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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Elsevier Ltd
01.11.2023
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| ISSN: | 0141-1187, 1879-1549 |
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| Abstract | •Modeling artificial upwelling efficiency under crossflow.•Optimized nozzle layout improves upwelling efficiency.•Discrete particle swarm algorithm enhances carbon sequestration.•Increasing nozzles will improve performance, but raises investment cost.•Reference for engineering design of artificial upwelling.
Artificial upwelling (AU) is widely recognized as a geoengineering tool to enhance oceanic fertility and stimulate the earth's capability of self-healing. Several air-lifted AU systems have been established to promote seaweed growth and facilitate carbon sequestration, for instance, in Norway and China. However, few researchers have addressed the critical issue of optimizing the layout of the underwater nozzle array (UNA), a key component of the air-lifted AU system. In this paper, we propose a novel method to optimize the layout of UNA by introducing and improving the discrete particle swarm optimization (DPSO) algorithm to guide the optimal nozzle positions. Furthermore, we develop the nutrient transport efficiency model to evaluate the performance of the AU system and apply it to investigate a typical AU system, namely the one in Aoshan Bay, China. Our results demonstrate that the proposed DPSO algorithm outperforms the conventional particle swarm optimization algorithm and the nutrient transport efficiency under the optimized UNA layouts was improved by an average of 14.5% to 35.0% compared to randomly generated layouts. These findings suggest that our optimization method could serve as a scientific reference for the engineering design of AU. |
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| AbstractList | •Modeling artificial upwelling efficiency under crossflow.•Optimized nozzle layout improves upwelling efficiency.•Discrete particle swarm algorithm enhances carbon sequestration.•Increasing nozzles will improve performance, but raises investment cost.•Reference for engineering design of artificial upwelling.
Artificial upwelling (AU) is widely recognized as a geoengineering tool to enhance oceanic fertility and stimulate the earth's capability of self-healing. Several air-lifted AU systems have been established to promote seaweed growth and facilitate carbon sequestration, for instance, in Norway and China. However, few researchers have addressed the critical issue of optimizing the layout of the underwater nozzle array (UNA), a key component of the air-lifted AU system. In this paper, we propose a novel method to optimize the layout of UNA by introducing and improving the discrete particle swarm optimization (DPSO) algorithm to guide the optimal nozzle positions. Furthermore, we develop the nutrient transport efficiency model to evaluate the performance of the AU system and apply it to investigate a typical AU system, namely the one in Aoshan Bay, China. Our results demonstrate that the proposed DPSO algorithm outperforms the conventional particle swarm optimization algorithm and the nutrient transport efficiency under the optimized UNA layouts was improved by an average of 14.5% to 35.0% compared to randomly generated layouts. These findings suggest that our optimization method could serve as a scientific reference for the engineering design of AU. |
| ArticleNumber | 103724 |
| Author | Qu, Mengjie Wen, Caining Lin, Zhenni Fan, Wei Zhang, Yiyuan Zhao, Yonggang |
| Author_xml | – sequence: 1 givenname: Yiyuan surname: Zhang fullname: Zhang, Yiyuan organization: Hainan Institute, Zhejiang University, Sanya 572000, China – sequence: 2 givenname: Wei surname: Fan fullname: Fan, Wei email: wayfan@zju.edu.cn organization: Hainan Institute, Zhejiang University, Sanya 572000, China – sequence: 3 givenname: Yonggang surname: Zhao fullname: Zhao, Yonggang organization: Ocean College, Zhejiang University, Zhoushan 316000, China – sequence: 4 givenname: Caining surname: Wen fullname: Wen, Caining organization: Hainan Institute, Zhejiang University, Sanya 572000, China – sequence: 5 givenname: Zhenni surname: Lin fullname: Lin, Zhenni organization: Hainan Institute, Zhejiang University, Sanya 572000, China – sequence: 6 givenname: Mengjie surname: Qu fullname: Qu, Mengjie organization: Hainan Institute, Zhejiang University, Sanya 572000, China |
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| CitedBy_id | crossref_primary_10_1016_j_ijnaoe_2025_100672 crossref_primary_10_1051_e3sconf_202458101024 crossref_primary_10_1051_e3sconf_202453703011 crossref_primary_10_1016_j_apor_2025_104756 crossref_primary_10_1016_j_oceaneng_2024_117980 crossref_primary_10_1051_e3sconf_202451101032 crossref_primary_10_1016_j_ast_2024_109803 crossref_primary_10_1038_s41598_024_58029_3 |
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| Keywords | Discrete particle swarm optimization Carbon sequestration Layout optimization Artificial upwelling |
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