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SAB-2型岸用光学测波仪在海洋站传统的使用是:d测,井以d进行查K表,再通过取潮位计算H’,(两种方法:(1)测实有潮位;(2)取潮汐表5的潮位。)然后再所算出H’·K·B值(按规范规定取小数后二位)。这个值实际上是波高修正系数,即对计测出的格值进行订正后便是该观测时次的波高和最大波高。这种求取订正值的方法,有两个弱点:(1)d的观测是利用仪器目测估计法,所测15个连续波的平均位置。当在小波时(指1.0m以下时),这个d不难求出,但在大波(1.0m 相似文献
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基于 SWAN 波浪传播模型建立包含风暴潮与天文潮耦合传播的台风浪数值模型,通过多次台风引起的波浪模拟,证实该模型可适用于浙江沿海.将1949年以来登陆我国大陆沿海最强的“5612”号台风作为典型的超强台风,计算了超强台风在浙北至浙南3个不同地点登陆遭遇天文潮高潮位时产生的沿海波高过程.结果显示,在开敞海区,登陆点南侧附近及其以北沿海,台风登陆时过程最大有效波高与风暴高潮位基本同时出现,而在登陆点以南远区的沿海海域,最大有效波高出现在登陆前的一个高潮位附近;超强台风作用下浙江陆域沿海离岸近1 km 范围内有效波高可达4耀6 m.这些结论对海堤工程设计和防灾减灾具有重要意义. 相似文献
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海滩冲流带高频振动地形动力过程分析 总被引：1，自引：0，他引：1
在粤东汕尾后江湾海滩冲流带布设2条观测剖面,共计6个观测点,对滩面冲流带在约一个潮周期内的高频振动进行了观测,取得采样频率分别为1min/次和6min/次的滩面数据各1组.结合同步观测的碎波带波浪潮汐数据,分析探讨了海滩高频振动特征.分析认为在涌浪条件下,滩面高频振动的日内变化主要受到潮位变化过程的控制,涨潮堆积,落潮侵蚀.利用交叉谱分析的结果表明滩面高程变化滞后于潮位变化.滩面下部比上部振动幅度大,变化复杂,滩角脊部比凹部活跃.波群对滩面高频振动有显著影响,特别是波高大于有效波高的波群.滩面高频振动没有表现出明显的泥沙逐渐向陆地堆积过程,有一定的振动周期. 相似文献
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波浪会对海床产生反复的作用力，由此引起的土体颗粒间孔隙水压力变化是造成土体液化的主要原因。使用自行研发的孔压监测设备，对黄河口埕岛海域易液化区海底孔压进行了长时间、高精度的观测，并对孔隙水压力、波高以及潮位间的关系进行分析。监测结果显示，本次监测条件下波浪最大作用深度介于0.5～1.5 m之间，超过该作用深度后孔压无明显变化。土体内部孔隙水压力的变化主要由潮位和波高决定，潮位的作用可使孔压缓慢平滑的变化且对超孔压无影响；波高的作用可使孔压快速、剧烈地振荡并导致超孔压的出现。 相似文献
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海平面上升对中国沿海工程的潮位和波高设计值的影响 总被引：4，自引：0，他引：4
本文根据国内外专家对中国近海海平面上升幅度的估计，讨论了海平面上升对海洋工程和海岸工程所需的设计潮位、设计波高分析计算的影响。 相似文献
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在潮差大的海区,为了最大限度利用空气动能,获得最高转换效率,波能发电装置应适应潮位变化,使装置始终保持最佳吃水状态。本文介绍一种波能发电装置随潮位变化而自动升降的控制方法,这种方法的特点是在汹涛海面上简便地建立了静水面,以便进行压力控制,经试验研究,只要采用两根直径不同的、上粗下细对接起来的圆管,当其截面积比在256:1以上时,内波高即可衰减到外波高的2.6%以下(相对系统设计波况及吃水情况下),可以满足一般海洋工程的精度要求.本方法对其它有关领域也可引以借鉴。 相似文献
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基于最新岸线水深数据,建立了霞关海域浪-潮-流耦合模型,模型经12次台风过程验证,模拟与实测较为一致。从6个影响苍南较严重的台风中,经比选确定1323号台风"菲特"为极限台风。基于极限台风,以22.5 km为单位平移形成18条路径,分10个强度等级,以极限台风风场驱动耦合模型计算了霞关渔港的风暴潮和海浪场,分析地形、风向和海流因素对渔港海域潮位和波高分布的影响。结合实测岸线高程,以最高潮位和波高组合估算各级台风下的漫堤情况,分段对渔港岸线防台等级进行评估,按照就低不就高的原则,确定霞关渔港岸线防台等级为11级。 相似文献
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基于第三代海浪模型SWAN,采用自嵌套的方法提供谱边界条件,对影响苏北辐射沙洲海域的一次冷空气过程和一次台风过程作用下的波浪进行了模拟。考虑到沙洲海域强潮水动力环境,分析了潮位和潮流的变化对该海域波浪的影响。结果表明,沙洲处波高和波周期受潮位影响显著,受潮流影响弱,具有潮周期起伏的特点,而波向受潮位潮流影响不显著;考虑高潮位后,以弶港为界,南北辐射沙洲波高显著增加的区域与波浪传播方向有关:波浪由北向南传播,相差不大,波浪由东向西传播,北部明显大于南部。 相似文献
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近岸潮位观测是海洋工程应用、海岸防灾减灾、海岸带管理以及海洋有关科研工作中最基础的工作之一。文章基于视频图像深度学习的方法,使用YOLOv5目标检测算法从安装在近岸的固定摄像机拍摄的视频帧中提取潮汐水位特征进行潮位分析。研究采用厦门高崎码头的分辨率为1920×1080的高清摄像头2023年2月的影像数据作为训练集和验证集,2023年3月的影像数据作为测试集,利用岸边验潮井逐时潮位数据进行标注,采用YOLOv5目标检测算法来训练。计算结果显示,通过视频观测潮位在训练集和测试集上的误差分别为3.9 cm和5.3 cm。视频中1个像素点代表3.8 cm,因此潮位观测的平均误差为像素级。研究表明在近岸通过高清摄像头基于图像深度学习进行潮位观测的方法是可行的,观测精度取决于图像目标物的分辨率。 相似文献
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In this paper, we present a mathematical model including seakeeping and maneuvering characteristics to analyze the roll reduction for a ship traveling with the stabilizer fin in random waves. The self-tuning PID controller based on the neural network theory is applied to adjust optimal stabilizer fin angles to reduce the ship roll motion in waves. Two multilayer neural networks, including the system identification neural network (NN1) and the parameter self-tuning neural network (NN2), are adopted in the study. The present control technique can save the time for searching the optimal PID gains in any sea states. The simulation results show that the present developed self-tuning PID control scheme based on the neural network theory is indeed quite practical and sufficient for the ship roll reduction in the realistic sea. 相似文献
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This paper presents a neural network (NN) controller for a fishing vessel rudder roll system. The aim of this study is to build a NN controller which uses rudder to regulate both the yaw and roll motion. The neural controller design is accomplished with using the classical back-propagation algorithm (CBA). Effectiveness of the proposed NN control scheme is compared with linear quadratic regulator (LQR) results by simulations carried out a fishing vessel rudder roll stabilizer system. 相似文献
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This paper deals with the application of nonparametric system identification to a nonlinear maneuvering model for large tankers using artificial neural network method. The three coupled maneuvering equations in this model for large tankers contain linear and nonlinear terms and instead of attempting to determine the parameters (i.e. hydrodynamic derivatives) associated with nonlinear terms, all nonlinear terms are clubbed together to form one unknown time function per equation which are sought to be represented by the neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag maneuvers and the proposed method has been applied to these data. The neural network scheme adopted in this work has one middle or hidden layer of neurons and it employs the Levenberg–Marquardt algorithm. Using the best choices for the number of hidden layer neurons, length of training data, convergence tolerance etc., the performance of the proposed neural network model has been investigated and conclusions drawn. 相似文献
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A wave-height meter using a simple microwave Doppler radar,simeq10 mW in power and 10.525 GHz in frequency, is proposed so that we can measure oceanic waves effectively while the ship is steaming. It was first applied to the measurement of the variation of water level generated in a wave tank, which suggested that it is adequately applicable to the measurement of oceanic waves. A field test was carried out off the cape of Nojimazaki by installing the Doppler radar 5 m above the sea level at the bow of the ship. The result agreed reasonably well with that measured simultaneously by the ultrasonic wave-height meter installed at the same position. Another test is running successfully on a larger ship with the wave-height meter installed at 9 m above the sea level. The significant wave height measured by the present meter is being compared with that observed visually by the navigation officers. 相似文献
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Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models
A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters. 相似文献
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Several control methods of wave energy converters (WECs) need prediction in the future of wave surface elevation. Prediction of wave surface elevation can be performed using measurements of surface elevation at a location ahead of the controlled WEC in the upcoming wave. Artificial neural network (ANN) is a robust data-learning tool, and is proposed in this study to predict the surface elevation at the WEC location using measurements of wave elevation at ahead located sensor (a wave rider buoy). The nonlinear autoregressive with exogenous input network (NARX NN) is utilized in this study as the prediction method. Simulations show promising results for predicting the wave surface elevation. Challenges of using real measurements data are also discussed in this paper. 相似文献
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In order to understand the features of coastal zone and to utilize the coastal areas, it is necessary to determine the sediment movement and the resulting transport. Waves, topographic features, and material properties are known as the most important factors affecting the sediment movement and coastal profiles. In this study, by taking into consideration of wave height and period (H0, T), bed slope (m) and sediment diameter (d50), cross-shore sediment movement was studied in a physical model and various bar-shape parameters of the resultant erosion type profile were determined. Using 80 experimental data which are obtained from physical model studies, a neural network (NN) has been calibrated to predict bar-shape parameters of beach profiles. A sensitivity analysis was firstly carried out to decide data of training and test sets. Four different models, in which the rates of their training and testing set data were 80% and 20%, 70% and 30%, 60% and 40%, 50% and 50% were constituted and their performances were compared. It was determined that the model, in which the rate of its training and testing set data was 80% and 20%, respectively, has the best results. Therefore, a total of 64 experimental data were used as training set and the remainders of the experimental data were used as a testing set for the model. The performance of the NN model was compared with the regression equations developed in a previous study and the equations cited in literature indicating better performance over the equations. 相似文献
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针对赤潮灾害等级预测难的现状,提出了一种基于C4.5决策树与二分分割算法优化的BP(反向传播)神经网络赤潮等级预测模型。该模型针对传统BP神经网络输入参数难以选择和隐含层节点数量难以确定的问题,通过决策树分类获取最优的属性组合,来解决输入参数难以选择的问题;通过"二分分割算法",来解决隐含层节点数难以确定的问题。实验结果表明,该模型在青岛近海海域赤潮灾害等级预测中,预测结果的均方根误差(RMSE)小于传统BP神经网络的预测误差,并且在网络训练时间上有所缩短,预测精度上有所提高,能够获得良好的预测结果,可为赤潮等级预测提供新的解决方法。 相似文献
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1 .Introduction Large civil engineering structures are exposed to various external loads such as earthquakes ,winds ,traffic and wave loads during their lifetime . The structures may become deteriorated and de-graded withtime in an unexpected way, which m… 相似文献