Journal of Ocean Engineering and Science最新文献

Ocean thermal energy conversion (OTEC) is a renewable energy source that uses differences in ocean water temperature between warm surface and cold depth to generate electricity. It is an essential link in the carbon neutrality chain and one of the rising sectors of the ocean energy. This paper provides an overview of studies on closed thermodynamic cycles and the numerous difficulties that OTEC technology faces. A description of the thermodynamic cycles incorporating mixed or pure working fluids, as well as the implications of different working fluids on cycle efficiency were also studied. Changes in condensing and evaporating temperatures induced by variations in heat resources affect the efficiency of cycles with pure working fluids. Several strategies, such as intermediate extraction regeneration and heat recovery of ammonia-depleted solution can increase the thermal efficiency with mixed working fluids. In addition, the impact of the ejector on the cycle's performance is examined. Finally, the efficiency-improving strategies are described and summarized. Thermodynamic efficiency can increase using suitable working fluids and taking steps to maximize the rate of ocean thermal energy. To establish which approach is the most effective, different methods have been evaluated and compared under identical operating conditions.

The sea surface reconstructed from radar images provides valuable information for marine operations and maritime transport. The standard reconstruction method relies on the three-dimensional fast Fourier transform (3D-FFT), which introduces empirical parameters and modulation transfer function (MTF) to correct the modulation effects that may cause errors. In light of the convolutional neural networks’ (CNN) success in computer vision tasks, this paper proposes a novel sea surface reconstruction method from marine radar images based on an end-to-end CNN model with the U-Net architecture. Synthetic radar images and sea surface elevation maps were used for training and testing. Compared to the standard reconstruction method, the CNN-based model achieved higher accuracy on the same data set, with an improved correlation coefficient between reconstructed and actual wave fields of up to 0.96-0.97, and a decreased non-dimensional root mean square error (NDRMSE) of around 0.06. The influence of training data on the deep learning model was also studied. Additionally, the impact of the significant wave height and peak period on the CNN model’s accuracy was investigated. It has been demonstrated that the accuracy will fluctuate as the wave steepness increases, but the correlation coefficient remains above 0.90, and the NDRMSE remains less than 0.11.

It is widely agreed that the insider's noncompliance to the marine information security policies has brought about a major security problem in the organizational context. Previous research has stressed the potential of remunerative control, i.e., reward, to better understand this problem. Few studies have been devoted to the exploration of the coupling incentive mechanism of tangible and intangible rewards that would induce insider's compliance behavior towards the marine information security policy. In the present study, we address this research gap by proposing a theoretical model that explains the optimal coupling incentive mechanism of these two different types of remunerative control. Our findings have delivered insightful implications for practice and research on how to improve the marine information security policy compliance in a more subtle way.

Liquified natural gas (LNG) bunkering simultaneous operations (SIMOPs) refers to the operations (such as cargo operations, port activities and ship maintenance) occurring around LNG bunkering. SIMOPs pose new risks to LNG bunkering, because the operations are dynamically interlocked in which the occurrence probabilities of potential consequences change at different times due to commencement or completion of specific SIMOP events. However, traditional static risk assessment approaches are not able to take the dynamic nature of these new risks into account. This article proposes a dynamic quantitative risk assessment (DQRA) methodology based on the Bayesian network (BN) to develop better understanding of dynamic risks of LNG bunkering SIMOPs. The methodology is demonstrated and evaluated through a truck-to-ship LNG bunkering case study. The results and discussion of the case study validate the utility of the proposed methodology and demonstrate that BNs are efficient in performing the probability calculations and are flexible in conducting causal diagnosis. The main innovation of this work is realizing the quantification of risks at different times, which reflects the most essential time-changing characteristics of risks associated with LNG bunkering SIMOPs.

This manuscript examines the recently developed conformable three-dimensional Wazwaz–Benjamin–Bona–Mahony (3D-WBBM) equation’s dynamical behavior in terms of its spatial and temporal variables. The governing equation is stretch for the Korteweg-de-Vries equation that represents the unidirectional propagation of small amplitude long waves on the surface of hydro magnetic and acoustic waves in a channel, especially for shallow water. Solitary wave solutions of various types, such as kink and shock, as well as singleton, combined solitons, and complex solitons, are all retrieved. Additionally, solutions to hyperbolic, exponential, and trigonometric functions are obtained through the use of recently developed methods, namely the Kudryashov method (KM), the modified Kudryashov method (MKM), and the new extended direct algebraic method (NEDAM). The study conducts a comparison of our findings to well-known findings, and concludes that the solutions reached here are novel. Additionally, the earned results are sketched in different shapes to demonstrate their dynamics as a function of parameter selection. We can assert from the obtained results that the applied techniques are simple, vibrant, and quite well, and will be helpful tool for addressing more highly nonlinear issues in various of fields, especially in ocean and coastal engineering. Furthermore, our findings are first step toward understanding the structure and physical behavior of complicated structures. We anticipate that our results will be highly valuable in better understanding the waves that occur in the ocean. We feel that this work is timely and will be of interest to a wide spectrum of experts working on ocean engineering models.