Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment
Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/2/357 |
| _version_ | 1827343403783815168 |
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| author | Lin Mu Haiwen Tu Xiongfei Geng Fangli Qiao Zhihui Chen Sen Jia Ruifei Zhu Tianyu Zhang Zhi Chen |
| author_facet | Lin Mu Haiwen Tu Xiongfei Geng Fangli Qiao Zhihui Chen Sen Jia Ruifei Zhu Tianyu Zhang Zhi Chen |
| author_sort | Lin Mu |
| collection | DOAJ |
| description | Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea. |
| first_indexed | 2024-03-07T22:25:47Z |
| format | Article |
| id | doaj.art-79b5a7aedc484cceace495b6864905a5 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-07T22:25:47Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-79b5a7aedc484cceace495b6864905a52024-02-23T15:23:23ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-02-0112235710.3390/jmse12020357Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift ExperimentLin Mu0Haiwen Tu1Xiongfei Geng2Fangli Qiao3Zhihui Chen4Sen Jia5Ruifei Zhu6Tianyu Zhang7Zhi Chen8College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, ChinaCollege of Marine Science and Technology, China University of Geosciences, Wuhan 430074, ChinaChina Waterborne Transport Research Institute, Beijing 100088, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaChina Precise Ocean Detection Technology Co., Ltd., Yichang 443005, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaChang Guang Satellite Tecnology Co., Ltd., Changchun 130102, ChinaCollege of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, ChinaNational Marine Environmental Forecasting Center, Beijing 100081, ChinaAnnually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.https://www.mdpi.com/2077-1312/12/2/357maritime search and rescuedrift experimentaircraft wreckagedrift prediction model |
| spellingShingle | Lin Mu Haiwen Tu Xiongfei Geng Fangli Qiao Zhihui Chen Sen Jia Ruifei Zhu Tianyu Zhang Zhi Chen Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment Journal of Marine Science and Engineering maritime search and rescue drift experiment aircraft wreckage drift prediction model |
| title | Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment |
| title_full | Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment |
| title_fullStr | Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment |
| title_full_unstemmed | Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment |
| title_short | Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment |
| title_sort | research on the drift prediction of marine floating debris a case study of the south china sea maritime drift experiment |
| topic | maritime search and rescue drift experiment aircraft wreckage drift prediction model |
| url | https://www.mdpi.com/2077-1312/12/2/357 |
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