Haiyang Dong

Target detection is an important research content in the radar field. At present, efforts are being made to optimize the precision of detection information. In this paper, we use the high pulse repetition frequency (HPRF) transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection. In addition, We also apply Walsh matrix and genetic algorithm (GA) to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak, which make the results more accurate. In a radar receiver, data rearrangement of echo signals is performed, and then pulse compression and moving target detection (MTD) are utilized to get the final velocity and range information of a target without velocity ambiguity and range ambiguity. Besides, a small transmitting pulse time width is adopted to reduce the working blind area, and two different high pulse repetition frequencies (HPRFs) are adopted to solve the problem of range eclipse. Simulation results finally prove the effectiveness and feasibility of the proposed method.

The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction.

Submarine pipelines are the lifelines of the national economy. Under the influence of typhoons, high-speed currents and waves continuously erode the seabed, leading to suspension or even rupture of pipelines. Therefore, it is of great importance to study the sediment transport under the action of waves and currents. A numerical model of sediment scouring and deposition combining wave and currents is established, which considered tidal current, storm surges, wind waves, and sediments in the East China Sea. Combining with the monitoring of the actual laying condition of pipelines, it is found that the area with the most serious scouring is around KP300. It is shown that the typhoon weather with high intensity and density will lead to the suspension of pipelines, which is noteworthy in the construction of marine engineering.

Changes in the water flow field will occur around bridge piers, leading to local scouring. When pipelines intersect bridge piers, this kind of scour may cause pipeline exposure or even suspension, thereby influencing the safety of pipelines. Experiments and numerical simulations were conducted to calculate the local scour depth around piers. This paper focuses on the influence of scour around the bridge piers and its hazards to the downstream submarine pipelines. The scour elevation could be used to estimate the influence of bridge piers on the safety of submarine pipelines. Three types of bridge piers were included in this numerical model. In order to ensure the safety of submarine pipelines, it is necessary to arrange the bridge piers and pipelines reasonably, and to monitor the scour at pipelines.

From the perspective of the demand of marine ecosystem service function, this paper collects, sorts out and analyzes the marine ecological restoration related work by reclamation survey, mainland coastline survey, island coastline survey, blue bay application, coastal zone restoration project, ecological assessment and restoration of reclamation projects in counties (cities and districts). The successful experience of marine ecological restoration is analyzed, summarized and explored to present a relatively complete and typical marine ecological restoration scheme. Through the case study of typical ecological restoration, the optimized marine ecological restoration procedure is established. According to the development status and problems of different sea areas and coastal zones, the most suitable marine ecological restoration scheme is found, so as to solve specific marine environmental problems, and provide case reference and theoretical basis for subsequent marine ecological restoration.

Observation on hydrological characteristics and sediment concentration in sea area around Liuheng Island were conducted in this research, and data analysis and summary were carried out on vertical tidal current flow and vertical sediment concentration. This research provided a scientific basis for optimizing the plane layout of Zhoushan Liuheng LNG terminal project.

Numerical simulations were conducted where both local scour below pipelines and flow fields around pipelines occur. The evolution, shape, and scale of local scouring at pipelines under different flow velocities and pipeline layouts were calculated. The research area is Huibieyang Sea of Zhejiang, China, where large numbers of submarine pipelines have been laid. Results show that typical accumulations of sand waves cannot form behind the pipelines in this study area due to their fine sediment size. Data from field observations at the water depth where the pipelines are located were collected in this study, on the basis of which preliminary analysis was made on the stability of the seabed and local scouring in the research area in recent years.

Accurate and reliable prediction of scour depth around bridge piers is essential for bridge engineering. The nondimensional parameters and artificial intelligence algorithms are combined to predict local scour depth. Based on the results of field observation and laboratory tests, five machine-learning models are applied and compared with the Hydraulic Engineering Circular No. 18 (HEC-18) formula, which is widely used in the United States. The results show that the machine-learning models are more accurate than the traditional HEC-18 formula and that the neural network models are more suitable for the prediction of bridge pier erosion than the linear regression model.