Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence
A marine oil spill produces oil-related hazardous material (OHM) which can cause damage to the marine ecological environment, and seriously affect coastal economic development such as tourism and aquaculture. The turbulent momentum and energy generated by the wave breaking process have a significant...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac9245 |
| _version_ | 1797747312460562432 |
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| author | Shibiao Fang Mu Lin Sen Jia Kuan Liu Darong Liu |
| author_facet | Shibiao Fang Mu Lin Sen Jia Kuan Liu Darong Liu |
| author_sort | Shibiao Fang |
| collection | DOAJ |
| description | A marine oil spill produces oil-related hazardous material (OHM) which can cause damage to the marine ecological environment, and seriously affect coastal economic development such as tourism and aquaculture. The turbulent momentum and energy generated by the wave breaking process have a significant effect on accelerating the mixing of OHM and seawater, which is one of the main factors in oil becoming sunken or submerged. In order to explore the influence of offshore wave breaking on the formation and transportation of OHM, the wave breaking process was simulated in a 2D laboratory flume, and the behavior process of OHM was identified and tracked in this paper. Five groups of breaking waves of different significant wave height (SWH) were set up in the experiment, and then OHM with the same density and mass was added, respectively, in order to observe the sinking process under the action of wave-induced turbulence. The results show that the turbulence intensity is closely related to the phase of the wave, the turbulence activity is violent at the wave crest, and the vertical distribution of the turbulent energy dissipation rate in the turbulent mixing zone remains basically unchanged. Under the actions of wave breaking and turbulence, the OHM’s submergence depth shows a good binomial growth trend. For SWH = 12.45 cm, the OHM stays under the water for nearly 2.32 s, and it reaches the deepest position of 0.165 m. Compared with SWH = 12.45 cm, the submergence depths for waves with SWHs of 20.61 cm, 26.81 cm, 32.32 cm, and 36.54 cm are increased by 8%, 37%, 80%, and 159%, respectively. Then, the submergence depths due to the other four waves are increased progressively, and the growth rates are 8%, 26%, 31%, 44%, respectively (compared with the same period of the previous wave). |
| first_indexed | 2024-03-12T15:49:57Z |
| format | Article |
| id | doaj.art-792326a12a7343bf9ba202d8601d5e82 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:49:57Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-792326a12a7343bf9ba202d8601d5e822023-08-09T15:16:45ZengIOP PublishingEnvironmental Research Letters1748-93262022-01-01171010405810.1088/1748-9326/ac9245Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulenceShibiao Fang0Mu Lin1Sen Jia2Kuan Liu3Darong Liu4https://orcid.org/0000-0001-7581-6192College of Computer Science and Software Engineering, Shenzhen University , Shenzhen 518060, People’s Republic of China; College of Life Sciences and Oceanography, Shenzhen University , Shenzhen 518060, People’s Republic of China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) , Guangzhou 511458, People’s Republic of ChinaCollege of Life Sciences and Oceanography, Shenzhen University , Shenzhen 518060, People’s Republic of China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) , Guangzhou 511458, People’s Republic of ChinaCollege of Computer Science and Software Engineering, Shenzhen University , Shenzhen 518060, People’s Republic of ChinaCollege of Computer Science and Software Engineering, Shenzhen University , Shenzhen 518060, People’s Republic of ChinaCollege of Marine Science and Technology, China University of Geosciences , 388 Lumo Road, Wuhan 430074, People’s Republic of ChinaA marine oil spill produces oil-related hazardous material (OHM) which can cause damage to the marine ecological environment, and seriously affect coastal economic development such as tourism and aquaculture. The turbulent momentum and energy generated by the wave breaking process have a significant effect on accelerating the mixing of OHM and seawater, which is one of the main factors in oil becoming sunken or submerged. In order to explore the influence of offshore wave breaking on the formation and transportation of OHM, the wave breaking process was simulated in a 2D laboratory flume, and the behavior process of OHM was identified and tracked in this paper. Five groups of breaking waves of different significant wave height (SWH) were set up in the experiment, and then OHM with the same density and mass was added, respectively, in order to observe the sinking process under the action of wave-induced turbulence. The results show that the turbulence intensity is closely related to the phase of the wave, the turbulence activity is violent at the wave crest, and the vertical distribution of the turbulent energy dissipation rate in the turbulent mixing zone remains basically unchanged. Under the actions of wave breaking and turbulence, the OHM’s submergence depth shows a good binomial growth trend. For SWH = 12.45 cm, the OHM stays under the water for nearly 2.32 s, and it reaches the deepest position of 0.165 m. Compared with SWH = 12.45 cm, the submergence depths for waves with SWHs of 20.61 cm, 26.81 cm, 32.32 cm, and 36.54 cm are increased by 8%, 37%, 80%, and 159%, respectively. Then, the submergence depths due to the other four waves are increased progressively, and the growth rates are 8%, 26%, 31%, 44%, respectively (compared with the same period of the previous wave).https://doi.org/10.1088/1748-9326/ac9245oil hazardous materialbreaking wavestransport and behaviorsubmergence depthdeep learning technique |
| spellingShingle | Shibiao Fang Mu Lin Sen Jia Kuan Liu Darong Liu Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence Environmental Research Letters oil hazardous material breaking waves transport and behavior submergence depth deep learning technique |
| title | Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence |
| title_full | Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence |
| title_fullStr | Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence |
| title_full_unstemmed | Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence |
| title_short | Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence |
| title_sort | deep learning technique for examining the mechanism transport and behavior of oil related hazardous material caused by wave breaking and turbulence |
| topic | oil hazardous material breaking waves transport and behavior submergence depth deep learning technique |
| url | https://doi.org/10.1088/1748-9326/ac9245 |
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