... ABSTRACT: In this paper, we propose a least square support vector machine (LSSVM) model to predict ocean wave elevations in a random sea state. The frequency and time domain characteristics of historical wave data are both considered in the proposed model. The wave data following a JONSWAP...

... ABSTRACT In this study, we explore the ability of the full connected neural network (FCNN) in prediction of phase-resolved uni-directional and multi-directional waves in various wave conditions in a typical offshore engineering application. The neural network models are trained on random waves...

... ABSTRACT Accurate prediction of the structural response of flexible cylinder vortex-induced vibrations (VIVs) relies heavily on the accuracy of the acquired hydrodynamic coefficient database. Due to a large number of variables, the construction of systematic hydrodynamic databases from rigid...

... ABSTRACT In order to accurately predict the dramatic changes of pressure around submarine fore region, a method of prediction on continuous time series variables based a multi-scale network is proposed. The multi-scale network is constituted by long short-term memory network (LSTM...

... and geosciences becomes more and more popular. Adyta et al. (2018) used the ARIMA model (Autoregressive Integrated Moving Average) to predict significant waves height obtained from SWAN numerical model with a high correlation exceeding 0.94. The results showed an accurate short-term prediction with ARIMA (2,2,2...

... ABSTRACT The main objective is to investigate the possibility that machine learning can be used in the real-time simulation of ship motion. A short-term prediction of seakeeping and maneuvering is strongly required for the navigation process. However, accurate and instant prediction remains...

... ABSTRACT Short-term prediction of ocean wave behavior is an effective technology for operating ships and offshore structures. It is possible to make medium to long-term predictions up to one week in advance, thanks to the development of the statistical approaches. On the other hand, using...

... ABSTRACT The airgap extremes are keys design parameters for the semisubmersible, while uncertainty remains in the airgap prediction due to the high complexity and nonlinearity of this physical mechanism, especially under severe weather conditions. The main objective of the present paper...

... the forecasts of SIC with other machine learning and deep learning models. The proposed model outperforms the compared models in terms of different metrics. The proposed VAE based Non-Autoregressive Transformer can be used for long-term SIC forecast and achieve stable and good accuracy predictions...

... artificial intelligence ship motion prediction wind velocity wind angle machine learning manoeuvre performance evaluation prediction reservoir simulation motion prediction model uncertainty ship model accurate ship motion prediction zigzag surge velocity trajectory different...

... ABSTRACT This work presents CFD predictions on ship added resistance and motion response in waves using the CFD solver snuMHLFoam, an in-house package developed on OpenFOAM. Both captive and free-running tests in waves are carried out for the container ship model (H-CNTR) to study the effects...

... ABSTRACT This study presents a numerical investigation for the wavelength effect on the parametric roll in head waves. The computational fluid dynamics (CFD) simulations are carried out for a model-scale ONR Tumblehome to predict the heave, roll, and pitch motions as the parametric roll occurs...

... diameter slurry pump fluid dynamics metals & mining e-e-dpm coefficient erosive wear particle equation interaction deep-sea mining pump ore particle reservoir simulation fraction transiently track particle mining pump lagrangian prediction upstream oil & gas ddpm...

... excitation force ocean energy wave excitation force deep learning machine learning prediction model regular wave wec artificial intelligence prediction neuron neural network wave force time step wave elevation coefficient amplitude equation optimal control prediction result...

... (RF), multilayer perceptron neural networks (MLPNN) and gradient boosting machine (GBM), in the prediction of fitting parameters. Results show that the GBM algorithm has the best performance in predicting fitting parameters among all considered ML algorithms. The relative importance of variables...

... are still functional, since at that operational stage, both SCADA and structural response can be collected concurrently. Once the strain sensors are not functional, the trained deep neural network is deployed, providing structural response predictions from on-site SCADA data. The proposed virtual monitoring...

... machine learning motion response heave network model artificial intelligence output data hot spot fatigue damage fatigue damage prediction neural network prediction sea state response spectrum calculation fatigue damage ship motion reference data mesh ABSTRACT A novel...

... compare the predictive values with NN and the conventional polynomial approximation method. INTRODUCTION The maneuvering motion of a ship is simulated by solving the motion equation. In the case of MMG model, see Yasukawa, H. and Yoshimura, Y. (2015), the external force term of this equation...

... are becoming increasingly important in the shipping industry. As manufactured goods are increasingly containerized, the container ship fleet has expanded. According to DNV's hull monitoring rules, it is required to provide a forecast prediction based on recent measurement data to alarm the captain...

... ABSTRACT Navigation situation prediction is of great significance in many scenarios. It will help improving efficiency and security in collision avoidance, berthing and unberthing and so on. In this paper, a Long Short-Term Memory (LSTM) neural network is applied to predict the navigation...