Predicting drift characteristics of persons-in-the-water in the South China Sea

Tu, Haiwen; Wang, Xiaodi; Mu, Lin; Xia, Kai

doi:10.1016/j.oceaneng.2021.110134

Cited by 9 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhu carried out drift experiments on typical nearshore fishing vessels using direct observation methods, analyzing the drift characteristics and patterns of these vessels when unpowered at sea [23]. It is evident that scholars have been conducting maritime drift experiments to study the drift characteristics and patterns of common sea-borne targets [24,25]. However, there is still a relative scarcity of drift experiments for specific oceanic areas, especially regarding floating debris, which necessitates further research in this area.…”

Section: Introductionmentioning

confidence: 99%

Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment

Mu,

Tu,

Geng

et al. 2024

JMSE

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment

Mu,

Tu,

Geng

et al. 2024

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…Xie proposed a target positioning and tracking method based on multiple submersibles [11]. Tu used field experiments combined with Monte Carlo simulations to determine the drift characteristics of humans in the South China Sea water [12]. Based on the background of maritime search and rescue (SAR), Wu proposed an autonomous coverage path planning algorithm based on deep reinforcement learning for search and rescue of drowning people at sea [13].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Rescue Model for Submersible Based on Monte Carlo Analysis and Genetic Algorithm

Zhou,

Chen,

et al. 2024

HSET

View full text Add to dashboard Cite

In recent years, there has been a growing interest in deep-sea exploration activities, yet significant gaps persist in search and rescue methodologies for deep-sea submersibles. This paper introduces a position prediction and search model for deep-sea submersibles, leveraging Monte Carlo analysis and genetic algorithms for optimization. The underwater motion of the submersible is simulated through dynamic system modeling. Monte Carlo analysis is utilized to model the scattering of the submersible within the specified ranges of longitude [10.25, 20.25], latitude [20.00, 36.50], and depth [2500m-4000m]. The analysis reveals a normal distribution of scattering points from the middle to the end. Genetic algorithms are employed to refine search and rescue paths, iterated 1000 times to identify the optimal path spanning from 540.1km to 225.6km. In comparison to complex numerical methods, this model offers ease of programming and implementation while accurately predicting the submersible's position in three-dimensional space. It provides the most efficient search and rescue route, ensuring swift response in the face of improbable communication or mechanical failures.

show abstract

“…So far, search operations in urban rivers have not been guided by any modeling technique aiming at predicting the trajectory of the victim's body. Existing computational studies of body drift trajectories were developed for marine environments (Breivik & Allen, 2008; Carniel et al., 2002; Gunduz, 2017; Hart‐Davis & Backeberg, 2023; Ličer et al., 2020; Mateus et al., 2015; Tu et al., 2021; Wu et al., 2023) and, as such, they focused on the effects of wind, waves, and currents. In most cases, the vertical body motion was neglected (Carniel et al., 2002).…”

Section: Introductionmentioning

confidence: 99%

Drift of a Drowning Victim in Rivers: Conceptualization and Global Sensitivity Analysis Under Idealized Flow Conditions

Delhez,

Rivière,

Erpicum

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

We present a conceptual model of the drift of a drowning victim in a river, accounting for the mechanical effects of body decomposition. The model was tested under idealized flow conditions (uniform flow) and a global sensitivity analysis was performed. Uncertainties in body morphology, flow‐body interaction parameters (e.g., drag coefficient) and drowning characteristics were considered. The results emphasize the importance of predicting the body vertical position since it considerably influences the streamwise body drift velocity, due to the significant difference between near‐surface and near‐bed velocity. The results also highlight the value of testimonies providing information on body mass and height, as decreasing uncertainty on these input data strongly reduces uncertainties on the predicted body streamwise position. Next step of the research will consist in coupling the model with an Eulerian multi‐dimensional model of river hydrodynamics.

show abstract

Predicting drift characteristics of persons-in-the-water in the South China Sea

Cited by 9 publications

References 14 publications

Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment

Research on the Drift Prediction of Marine Floating Debris: A Case Study of the South China Sea Maritime Drift Experiment

Comprehensive Rescue Model for Submersible Based on Monte Carlo Analysis and Genetic Algorithm

Drift of a Drowning Victim in Rivers: Conceptualization and Global Sensitivity Analysis Under Idealized Flow Conditions

Contact Info

Product

Resources

About