Determining the drift characteristics of open lifeboats based on large-scale drift experiments

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

doi:10.3389/fmars.2022.1017042

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…However, during validation of the case study it is deemed that the IAMSAR recommendation of leeway should be a better representation of a real case scenario. There is also researchers performing field experiments of drifting objects [2] that point to a very slow drift of approximately a tenth of the wind speed, presently supported by [58].…”

Section: Scenario Contextmentioning

confidence: 99%

Model Fidelity in Mission Scenario Simulations for Systems of Systems : A Case Study of Maritime Search and Rescue

Schön

2023

Linköping Studies in Science and Technology. Licentiate Thesis

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Systems of Systems (SoS) represent a view where individual, independent systems named Constituent System (CS) collaborate to achieve overarching capabilities not attainable by a single CS alone. While CS can traditionally be engineered entities like boats or aircraft, they might also encompass human actors, positioning entire organizations to be viewed as an SoS. There is a large interest in modeling and simulation of phenomena present at different scales of SoS, which requires choosing appropriate model fidelity and resolution for the study. This thesis delves into the intricacies of modeling and simulation of mission scenarios within SoS, with a keen focus on engineered CS operating in physical environments. At the heart of the thesis lies the concept of model fidelity, examined through philosophical, scientific, and engineering perspectives. A maritime search and rescue scenario by airborne search assets serves as case study, using Agent Based Simulation (ABS) to explore the Measures of Effectiveness (MoE) of the mission. Through a hierarchical viewpoint it is studied how the model fidelity on Sub-System (SS) level affects its performance and how it aggregates up to performance on the CS level and at last the MoE on SoS level. Search scenarios are tested with different weather conditions and navigation approaches First of all, I want to express my gratitude to my supervisors who have supported me throughout this first leap of research studies, Petter Krus, Christopher Jouannet, Carina Marcus, and Kristian Amadori. You have all inspired me in research, by interesting and detailed technical discussions, by encouraging me to view research from different perspectives, and also by thoughts about life and balance in an academic setting. My research journey has so far been both exciting, challenging and brought about change in more areas than research. When I entered doctoral studies, the Systems of Systems research was shared by both divisions with a couple of us working at product realisation and a couple at flumes, it has been a great joy to get to know all of you and it certainly broadened my perspectives. I want to dedicate a special thank you to the design automation laboratory and to my PhD peers, former and present. Especially to you who kept company during the pandemic, by chatting, sending memes, and joining afternoon walks. Lastly but most importantly, I want to express my deepest gratitude to my family who are always there to support me through any challenges. Thank you, for traveling across the country to encourage me in these endeavors. No matter the distance, you are always close. Karlskrona, Oktober 2023

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Section: Scenario Contextmentioning

confidence: 99%

Model Fidelity in Mission Scenario Simulations for Systems of Systems : A Case Study of Maritime Search and Rescue

Schön

2023

Linköping Studies in Science and Technology. Licentiate Thesis

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“…For this study, this paper set out to use the Monte Carlo algorithm and six degrees of freedom kinematic equations. Tu H W et al used Monte Carlo methods to compare and evaluate lifeboat drift prediction models [3] . Goldfrank J et al applied the Monte Carlo method to study target tracking on offshore mobile platforms [4] .…”

Section: Introductionmentioning

confidence: 99%

Optimizing Search and Rescue Strategies for Deep-Sea Submersibles Using the Monte Carlo Method

Wei,

Ye,

Yang

2024

HSET

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Deep-sea exploration is an important field where humans challenge the limits of nature. Improving its safety and efficiency is of great significance to deep-sea scientific research and resource development. In response to this, this study established a Monte Carlo-based motion model to predict the location of the crashed submersible. To truly simulate the distress environment of the submersible, this study introduced the Monte Carlo method to randomly generate 2,000 particles to reflect the uncertainty of the initial distress location, ocean current direction, and seawater density. Finally, it was combined with a six-degree-of-freedom kinematic model to generate All possible time-varying trajectories. This study also compared the effects before and after using the corresponding equipment and found that the equipment has a good effect on reducing uncertainties. This paper expands the field focused on floating objects on the sea surface to the search for underwater targets.

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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories

Lin,

Yu,

Lian

2024

JMSE

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Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.

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Determining the drift characteristics of open lifeboats based on large-scale drift experiments

Cited by 4 publications

References 22 publications

Model Fidelity in Mission Scenario Simulations for Systems of Systems : A Case Study of Maritime Search and Rescue

Model Fidelity in Mission Scenario Simulations for Systems of Systems : A Case Study of Maritime Search and Rescue

Optimizing Search and Rescue Strategies for Deep-Sea Submersibles Using the Monte Carlo Method

Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories

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