Improved Maneuvering-Based Mathematical Model for Free-Running Ship Motions in Following Waves Using High-Fidelity CFD Results and System-Identification Technique

Araki, Mikiya; Sadat-Hosseini, Hamid; Sanada, Yasushi; Umeda, Naoya; Stern, Frederick

doi:10.1007/978-3-030-00516-0_6

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Final method, system identification, is the process of deriving the parameters of the ship motion equations describing a ship's characteristics through the application of mathematical optimization [16]. In contrast to a large number of captive model tests, the SI method provides an opportunity for the estimation of all coefficients and parameters with the use of a single or a few free-running tests [17]. While identification can be achieved through model-scale free-running tests, SI can also be accomplished with full-scale sea trials and simulation data [18].…”

Section: Introductionmentioning

confidence: 99%

Application of an offline grey box method for predicting the manoeuvring performance

Atasayan,

Milanov,

Dursun Alkan

2024

Brodogradnja

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The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship.

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Section: Introductionmentioning

confidence: 99%

Application of an offline grey box method for predicting the manoeuvring performance

Atasayan,

Milanov,

Dursun Alkan

2024

Brodogradnja

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“…In the present work, the objective of the DMD is the analysis and forecast of the finite-dimensional set of trajectory/motion/force time histories of ships operating in waves, offering a complementary efficient method to equation-based system identification approaches, e.g., Araki et al (2012Araki et al ( , 2019. The efficiency of the method in this context stems from the finite dimensionality of the set of relevant state variables together with the simplicity of operations required to model the system dynamics (as opposed to more data/resourceconsuming machine learning approaches).…”

Section: Introductionmentioning

confidence: 99%

Time-series forecasting of ships maneuvering in waves via dynamic mode decomposition

Diez

Serani

Campana

et al. 2022

J. Ocean Eng. Mar. Energy

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A data-driven and equation-free approach is proposed and discussed to forecast responses of ships maneuvering in waves, based on the dynamic mode decomposition (DMD). DMD is a dimensionality-reduction/reduced-order modeling method, which provides a linear finite-dimensional representation of a possibly nonlinear system dynamics by means of a set of modes with associated oscillation frequencies and decay/growth rates. This linear representation is entirely derived from available data and does not require the knowledge of the underlying system equations, which are and remain unknown. Based on the linear representation, DMD allows for short-term future estimates of the system state, which can be used for real-time prediction and control. Here, the objective of the DMD is the analysis and forecast of the trajectories/motions/forces of ships operating in waves, offering a complementary efficient method to equation-based system identification approaches. Results are presented for the course keeping of a free-running naval destroyer (5415M) in irregular stern-quartering waves and for the free-running KRISO Container Ship performing a turning circle in regular waves. Results are overall promising and show how DMD is able to identify the most important modes and forecast the system state with reasonable accuracy upto two wave encounter periods.

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“…The resulting model can run conditions not considered in its development to expand the analysis to new conditions. In prediction of ship motions in waves, different implementations of SI have been considered, ranging from coefficient-based mathematical models developed in Araki et al (2019) where parameters were tuned with CFD simulations to neural network-based models. Hess (2007) presented a method with Recurrent Neural Networks (RNN) that attempted to predict the 6-DoF motions of a full-scale ship operating in random waves.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

Silva¹,

Maki²

2021

Preprint

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Critical evaluation and understanding of ship responses in the ocean is important for not only the design and engineering of future platforms but also the operation and safety of those that are currently deployed. Simulations or experiments are typically performed in nominal sea conditions during ship design or prior to deployment, to evaluate different hullforms and develop operational profiles. Though it is necessary to analyze the response for nominal conditions, the results may not be reflective of the instantaneous state of the vessel and the ocean environment while deployed. Short-term temporal predictions of ship responses given the current wave environment and ship state would enable enhanced decision-making onboard and reduce the overall risk during operations for both manned and unmanned vessels, especially as the marine industry trends towards more autonomy. However, the current state-of-the-art in numerical hydrodynamic simulation tools are too computationally expensive to be employed for real-time ship motion forecasting and the computationally efficient tools are too low fidelity to provide accurate responses. Thus, a methodology is needed to provide fast and efficient predictions with levels of accuracy closer to the higher-fidelity tools.A methodology is developed with long short-term memory (LSTM) neural networks to represent the motions of a free running David Taylor Model Basin (DTMB) 5415 destroyer operating at 20 knots in Sea State 7 stern-quartering irregular seas. Case studies are performed for both course-keeping and turning circle scenarios. An estimate of the vessel's encounter frame is made with the trajectories observed in the training dataset. Wave elevation time histories are given by artificial wave probes that travel with the estimated encounter frame and serve as input into the neural network, while the output is the 6-DOF temporal ship motion response. Overall, the neural network is able to predict the temporal response of the ship due to unseen waves accurately, which makes this methodology suitable for system identification and real-time ship motion forecasting. The methodology, the dependence of model accuracy on wave probe and training data quantity and the estimated encounter frame are all detailed.

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Improved Maneuvering-Based Mathematical Model for Free-Running Ship Motions in Following Waves Using High-Fidelity CFD Results and System-Identification Technique

Cited by 5 publications

References 14 publications

Application of an offline grey box method for predicting the manoeuvring performance

Application of an offline grey box method for predicting the manoeuvring performance

Time-series forecasting of ships maneuvering in waves via dynamic mode decomposition

Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks

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