Journal of Ocean Engineering and Science最新文献

This paper deals with an optimal Kalman-like filter for nonlinear discrete-time systems aided with auto and cross-correlated noises and stochastic parameter matrices involved in state and measurement equations, and random nonlinearity. The random variables are proposed by their statistical characteristics while the inquiry is focused on stochastic multivariate analysis and calculation. For the nonlinear system with the auto and cross-correlated noises and stochastic parameter matrices, an equivalent system is first reconstructed by decomposing stochastic parameter matrices and introducing uncorrelated pseudo-noises. Then a recursive filter that ensures unbiasedness and minimizes the error variance is designed for the newly transformed equivalent system. Finally, the filter is verified by applying it to some numerical simulations.

In this paper, we discussed the enhanced Kudryashov’s and general projective Riccati equations techniques for obtaining exact solutions to the fifth-order nonlinear water wave (FONLWWE) equation. Using the enhanced Kudryashov’s method, we were able to achieve solitary wave and singular soliton solutions. Solitary-shock hybrid wave, singular soliton, and periodic wave solutions were discovered when we employed the general projective Riccati equations approach. We can say the given methods are effective and powerful for obtaining exact solutions. Our findings in this paper are critical for explaining a wide range of scientific and oceanographic applications involving ocean gravity waves and other related phenomena.

In this work, we studied a (2 + 1)-dimensional Sawada-Kotera equation (SKE). This model depicts nonlinear wave processes in shallow water, fluid dynamics, ion-acoustic waves in plasmas and other phenomena. A couple of well-established techniques, the Bäcklund transformation based on the modified Kudryashov method, and the Sardar-sub equation method are applied to obtain the soliton wave solution to the (2 + 1)-dimensional structure. To illustrate the behavior of the nonlinear model in an appealing fashion, a variety of travelling wave solutions are formed, such as contour, 2D, and 3D plots. The proposed approaches are proved more convenient and dominant for getting analytical solutions and can also be implemented to a variety of physical models representing nonlinear wave phenomena.

The long-term durability of glass fiber reinforced polymer (GFRP) bars in harsh alkaline environments is of great importance in engineering, which is reflected by the environmental reduction factor in various structural codes. The calculation of this factor requires robust models to predict the residual tensile strength of GFRP bars. Therefore, three robust metaheuristic algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM), were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system (ANFIS) in order to obtain more accurate prediction model. Various optimized models were developed to predict the tensile strength retention (TSR) of degraded GFRP rebars in typical alkaline environments (e.g., seawater sea sand concrete (SWSSC) environment in this study). The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging. A total number of 715 experimental laboratory samples were collected in a form of extensive database to be trained. K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal folds. In order to analyze the efficiency of the metaheuristic algorithms, multiple statistical tests were performed. It was concluded that the ANFIS-SVM-based model is robust and accurate in predicting the TSR of conditioned GFRP bars. In the meantime, the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete environment. The sensitivity analysis revealed GFRP bar size, volume fraction of fibers, and pH of solution were the most influential parameters of TSR.

This paper proposes a temporal-fractional porous medium model (T-FPMM) for describing the co-current and counter-current imbibition, which arises in a water-wet fractured porous media. The correlation between the co-current and counter-current imbibition for the fractures and porous matrix are examined to determine the saturation and recovery rate of the reservoir. For different fractional orders in both porous matrix and fractured porous media, the homotopy analysis technique and its stability analysis are used to explore the parametric behavior of the saturation and recovery rates. Finally, the effects of wettability and inclination on the recovery rate and saturation are studied for distinct fractional values.

Nowadays seakeeping is mostly analyzed by means of model testing or numerical models. Both require a significant amount of time and the exact hull geometry, and therefore seakeeping is not taken into account at the early stages of ship design. Hence the main objective of this work is the development of a seakeeping prediction tool to be used in the early stages of ship design.

This tool must be fast, accurate, and not require the exact hull shape. To this end, an artificial intelligence (AI) algorithm has been developed. This algorithm is based on Artificial Neural Networks (ANNs) and only requires a number of ship coefficients of form.

The methodology developed to obtain the predictive algorithm is presented as well as the database of ships used for training the ANN. The data were generated using a frequency domain seakeeping code based on the boundary element method (BEM). Also, the AI predictions are compared to the BEM results using both, ship hulls included and not included in the database.

As a result of this work it has been obtained an AI tool for seakeeping prediction of conventional monohull vessels

Having estimates of wave climate parameters and extreme values play important roles for a variety of different societal activities, such as coastal management, design of inshore and offshore structures, marine transport, coastal recreational activities, fisheries, etc. This study investigates the efficiency of a state-of-the-art spatial neutral gas clustering method in the classification of wind/wave data and the evaluation of extreme values of significant wave heights (Hs), mean wave direction (MWD) and mean wave periods (T0) for two 39-year time periods; from 1979 to 2017 for the present climate, and from 2060 to 2098, for a future climate change scenario in the Northwest Atlantic. These data were constructed by application of a numerical model, WAVEWATCHIII™ (hereafter, WW3), to simulate the wave climate for the study area for both present and future climates. Data from the model was extracted for the wave climate, in terms of the wave parameters, specifically Hs, MWD and T0, which were analyzed and compared for winter and summer seasons, for present and future climates. In order to estimate extreme values in the study area, a Natural Gas (hereafter, NG) clustering method was applied, separate clusters were identified, and corresponding centroid points were determined. To analyze data at each centroid point, time series of wave parameters were extracted, and using standard stochastic models, such as Gumbel, exponential and Weibull distribution functions, the extreme values for 50 and 100-year return periods were estimated. Thus, the impacts of climate change on wave regimes and extreme values can be specified.