Experimental studies on performances of flexible floating oil booms in coupled wave-current flow

Floating oil booms are commonly-adopted facility to collect spilled oil on sea surface, or to protect specific areas against oil slick spreading. In this study, 931 runs of laboratory test were carried out under wave-current coupling conditions to investigate hydrodynamic performances of the flexible floating oil boom. The tests first conducted a comparison on motion responses between the flexible floating boom and the rigid one to indicate the necessity of taking the flexibility of boom into consideration. Then a comprehensive analysis was carried out to investigate the effects of the ambient currents, waves and the boom characteristics of material stiffness, diameter of floater, length of skirt, and B/W (Buoyancy/Weight) ratio on the motion responses of the flexible floating booms. Finally, by taking the water blockage effect in front of the boom into consideration in the definition of boom effectiveness, the effective draft and freeboard were compared between the flexible boom and rigid one under fixed current and wave conditions. The effects of currents, waves, skirt lengths and B/W ratios on the effective draft and effective freeboard are assessed.     

Seakeeping performance of a containment boom section in random waves and currents

The seakeeping characteristics of various boom geometries in irregular waves and currents are investigated. The response of a floating boom section on the open sea is a function of a number of parameters, such as boom geometry, distribution of mass, buoyancy/weight ratio, and wave and current characteristics. To understand the relationship between these design parameters more clearly, a series of regular and irregular wave tests were conducted with six different 1:4 scale models for three current velocities and six different wave conditions. To simplify the problem, only rigid boom sections consisting of a buoyancy cylinder and vertical skirt were used. In parallel with this experimental program, a numerical model for the responses of two-dimensional floating boom sections in small-amplitude waves is also developed. The numerical results are compared with our large-scale experimental results. The boom effectiveness on the open sea is evaluated based on the concept of “effective draft” and “effective freeboard” assuming that drainage and oversplashing failures are the prime mechanisms of containment failure. Using the present results, a guideline for the optimum design/selection of future booms is developed.     

Laboratory investigation of single floating booms a and series of booms in the prevention of oil slick and jellyfish movement

Laboratory experiments were carried out to test the effectiveness of single floating booms, and series of barriers in preventing oil slick and jellyfish movement under various current, wind, and wave conditions. Samples of Kuwait light crude, Kuwait heavy crude, and emulsified Kuwait heavy crude oil were selected for the test program. It was found that the single floating booms were only effective in preventing the oil slick movement at current speeds less than 0.15 m/s for emulsified oil and 0.25 nits for non-emulsified oil. Double floating booms (with a distance of 16 times the boom's draught between them) prevented both non-emulsified and emulsified oil movement when current speed was less than 0.25 nits. The double floating boom system had the best performance in containing oil slick movement.In the combined air-bubble barrier and floating boom system, the air-bubble plume lifted the jellyfish to the water's surface, and thus most jellyfish accumulated in front of the floating boom. With the help of a proper collection device (such as a suction pump), the accumulated jellyfish could be removed to a desired location. Therefore, the quantity of jellyfish moving into the intake channel was reduced.Based on the test results, three different arrangements were recommended for the protection Kuwait's water intakes and harbors from oil slick and jellyfish movement.     

Numerical modelling of boom and oil spill with SPH

The protection of coastal areas against oil pollution is often addressed with the use of floating booms. These bodies are subject to an empirical design always based on physical models. Indeed, the numerical modelling of a two-phase flow (oil and water) with complicated free surface in the vicinity of a floating body is a challenging issue. The Smoothed Particle Hydrodynamics (SPH) Lagrangian numerical method is appropriate to such simulations since it allows the modelling of complex motions and fluid–structure interactions. In this paper we first study the mechanism of oil leakage by entrainment due to combined turbulent production and buoyancy. Then, we present the main features of the SPH method in a turbulent formalism and apply this model to predict the motion of a boom and an oil spill in an open-channel and a wave flume, for three types of oil (heavy, light and emulsion). Numerical results are compared to experiments and used to depict criteria for oil leakage. It appears that oil leakage by entrainment occurs when the surface water velocity upstream the boom exceeds a critical value which was estimated around 0.5 m/s for a light oil under steady current. A more accurate criterion is derived from theoretical considerations and successfully compared to numerical experiments. In the case of wave flume, no validation from experiments could be made. However, it appears that leakage occurs from a critical wave height between 0.5 and 1.0 m, for the tested wave period of 4 s. A more extended panel of numerical tests would allow a better knowledge of the involved mechanisms and critical parameters. An extensive use of this model should extend our knowledge regarding the mechanisms of oil leakage under a boom and allow a better and easier design of booms in the near future.     

Efficiency of OWC wave energy converters: A virtual laboratory

The performance of an oscillating water column (OWC) wave energy converter depends on many factors, such as the wave conditions, the tidal level and the coupling between the chamber and the air turbine. So far most studies have focused on either the chamber or the turbine, and in some cases the influence of the tidal level has not been dealt with properly. In this work a novel approach is presented that takes into account all these factors. Its objective is to develop a virtual laboratory which enables to determine the pneumatic efficiency of a given OWC working under specific conditions of incident waves (wave height and period), tidal level and turbine damping. The pneumatic efficiency, or efficiency of the OWC chamber, is quantified by means of the capture factor, i.e. the ratio between the absorbed pneumatic power and the available wave energy. The approach is based on artificial intelligence—in particular, artificial neural networks (ANNs). The neural network architecture is chosen through a comparative study involving 18 options. The ANN model is trained and, eventually, validated based on an extensive campaign of physical model tests carried out under different wave conditions, tidal levels and values of the damping coefficient, representing turbines of different specifications. The results show excellent agreement between the ANN model and the experimental campaign. In conclusion, the new model constitutes a virtual laboratory that enables to determine the capture factor of an OWC under given wave conditions, tidal levels and values of turbine damping, at a lower cost and in less time than would be required for conventional laboratory tests.     

波浪与海流测量仪器测试装置

"波浪与海流测量仪器测试装置"依靠机械传动机构带动传感器作相对运动,模拟"波浪"和"海流"测量状态。测试装置为实现实验室内进行声学测波仪和声学矢量海流计的调机、考机、检测增添了有效可靠的技术手段。     

缺失风浪数据补足的神经网络模型

基于波浪数据的完备性对于海岸海洋工程设计而言非常关键,详细阐述了风浪观测数据补足神经网络模型的建立方法,构建了两个网络模型,以已有观测资料为样本进行了验证.结果表明,两个网络的训练效果均很好,且单输出目标的分层模拟要优于多输出目标的单层模拟.表明了利用人工神经网络推导缺失波浪条件的可行性.     

Prediction of breaking waves with neural networks

The height of a wave at the time of its breaking, as well as the depth of water in which it breaks, are the two basic parameters that are required as input in design exercises involving wave breaking. Currently the designers obtain these values with the help of graphical procedures and empirical equations. An alternative to this in the form of a neural network is presented in this paper. The networks were trained by combining the existing deterministic relations with a random component. The trained network was validated with the help of fresh laboratory observations. The validation results confirmed usefulness of the neural network approach for this application. The predicted breaking height and water depth were more accurate than those obtained traditionally through empirical schemes. Introduction of a random component in network training was found to yield better forecasts in some validation cases.     

Wave hindcasting by coupling numerical model and artificial neural networks

By coupling numerical wave model (NWM) and artificial neural networks (ANNs), a new procedure for wave prediction is proposed. In many situations, numerical wave modeling is not justified due to economical consideration. Although incorporation of an ANN model is inexpensive, such a model needs a long time period of wave data for training, which is generally inconvenient to achieve. A proper combination of these two methods could carry the potentials of both. Based on the proposed approach, wave data are generated by a NWM by means of a short period of assumed winds at a concerned point. Then, an ANN is designed and trained using the above-mentioned generated wind-wave data. This ANN model is capable of mapping wind-velocity time series to wave height and period time series with low cost and acceptable accuracy. The method was applied for wave hindcasting to two different sites; Lake Superior and the Pacific Ocean. Simulation results show the superiority of the proposed approach.     

Real time wave forecasting using neural networks

Forecasting of ocean wave heights, with warning time of a few hours or days, is necessary in planning many operation-related activities in the ocean. Such information is currently derived by numerically solving the differential equation representing wave energy balance. The solution procedure involved is extremely complex and calls for very large amounts of meteorological and oceanographic data. This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site. It incorporates the technique of neural networks. The network involved is first trained by different algorithms and then used to forecast waves with lead times varying from 3 to 24 h. The results of different training algorithms are compared with each other. The neural output is further compared with the statistical AR models.     

Real time wave forecasting using neural networks

Forecasting of ocean wave heights, with warning time of a few hours or days, is necessary in planning many operation-related activities in the ocean. Such information is currently derived by numerically solving the differential equation representing wave energy balance. The solution procedure involved is extremely complex and calls for very large amounts of meteorological and oceanographic data. This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site. It incorporates the technique of neural networks. The network involved is first trained by different algorithms and then used to forecast waves with lead times varying from 3 to 24 h. The results of different training algorithms are compared with each other. The neural output is further compared with the statistical AR models.     

Neural networks for wave forecasting

The physical process of generation of waves by wind is extremely complex, uncertain and not yet fully understood. Despite a variety of deterministic models presented to predict the heights and periods of waves from the characteristics of the generating wind, a large scope still exists to improve on the existing models or to provide alternatives to them. This paper explores the possibility of employing the relatively recent technique of neural networks for this purpose. A simple 3-layered feed forward type of network is developed to obtain the output of significant wave heights and average wave periods from the input of generating wind speeds. The network is trained with different algorithms and using three sets of data. The results show that an appropriately trained network could provide satisfactory results in open wider areas, in deep water and also when the sampling and prediction interval is large, such as a week. A proper choice of training patterns is found to be crucial in achieving adequate training.     

A virtual laboratory for stability tests of rubble-mound breakwaters

The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project.     

Effects of new variables on the overtopping discharge at steep rubble mound breakwaters — The Zeebrugge case

This paper describes on the one hand parametric tests on wave overtopping for a steep rubble mound breakwater in Zeebrugge, Belgium. On the other hand the comparison between prototype measurements at the breakwater and their scale reproductions in two laboratories is dealt with. The objective is to gain information on possible scale and model effects for wave overtopping from this comparison. The prototype measurements are described together with the resulting dataset of 11 storms where wave overtopping occurred. Scale models and the laboratory measurements are described into detail mentioning similarities and differences to the prototype. Several model effects are identified and special attention is given to wind effects and to the placement pattern of the armour units, respectively. Monte Carlo simulations have been performed to get an idea about the influence of selected model uncertainties. Finally, scale effects are discussed and the influence of model and scale effects for the performed tests is quantified. Recommendations on how to treat these effects are presented.     

Hydrodynamic behavior of curtainwall-pile breakwaters

A numerical model is developed that can predict the interaction of regular waves normally incident upon a curtainwall-pile breakwater; the upper part of which is a vertical wall and the lower part consists of an array of vertical piles. The numerical model is based on an eigenfunction expansion method, and utilizes a boundary condition nearby the vertical piles that accounts for wave energy dissipation. Numerical solution comprises a finite number of terms, which is a superposition of propagating waves and a series of evanescent waves. The modeling is validated by comparison with previous experimental studies and overall agreement between measurement and calculation is fairly good. The numerical results are related to reflection, transmission, and dissipation coefficient; wave run-up, wave force, and wave overturning moment are also presented. Effect of porosity, relative draft, and relative water depth are discussed; the choice of suitable range of them is described. The relative draft is more effective for shallow water waves. Model shows decrease in relative draft and leads to reduction of relative wave force, overturning moment, and runup. It is shown that curtainwall-pile breakwaters can operate both effectively and efficiently in the range of relative draft between 0.15 and 0.75. The range 0.5 to 0.2 is also recommended for porosity.     

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991–2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007–2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables.     

基于核函数NPSSB模型的HY-2A卫星雷达高度计海况偏差研究

利用机器学习的方法,对14个周期HY-2A卫星高度计数据:风速、有效波高和海面高度差值进行训练,探究海况偏差和风速、有效波高之间的关系,创建海况偏差核函数非参数模型(NPSSB),并与参数模型中具有代表性的BM3、BM4模型进行对比。研究表明:(1)核函数NPSSB模型能够很好的反映SSB与U、SWH之间的关系,SSB与U呈二次函数关系,SSB与SWH呈反比例函数关系;(2)核函数NPSSB模型对SSB的模拟能力与训练数据集相关,数据量越多,模拟能力越好;(3)核函数NPSSB模型与BM3、BM4模型都存在0^-0.03 m的差值,随着风速和有效波高的增加,差值的绝对值越大。     

Wave reflection and transmission by curtainwall–pile breakwaters using circular piles

This paper presents a mathematical model which computes the hydrodynamic characteristics of a curtainwall–pile breakwater (CPB) using circular piles, by modifying the model developed for rectangular piles by Suh et al. [2006. Hydrodynamic characteristics of pile-supported vertical wall breakwaters. Journal of Waterway, Port, Coastal and Ocean Engineering 132(2), 83–96]. To examine the validity of the model, laboratory experiments have been conducted for CPB with various values of draft of curtain wall, spacing between piles, and wave height and period. Comparisons between measurement and prediction show that the mathematical model adequately reproduces most of the important features of the experimental results. The mathematical model based on linear wave theory tends to over-predict the reflection coefficient as the wave height increases. As the draft of the curtain wall increases and the porosity between piles decreases, the reflection and transmission coefficient increases and decreases, respectively, as expected. As the relative water depth increases, however, the effect of porosity disappears because the wave motion is minimal in the lower part of a water column for short waves.     

Influence of Regular Wave and Ship Characteristics on Mooring Force Prediction by Data-Driven Model

The study of mooring forces is an important issue in marine engineering and offshore structures. Although being widely applied in mooring system, numerical simulations suffer from difficulties in their multivariate and nonlinear modeling. Data-driven model is employed in this paper to predict the mooring forces in different lines, which is a new attempt to study the mooring forces. The height and period of regular wave, length of berth, ship load, draft and rolling period are considered as potential influencing factors. Input variables are determined using mutual information(MI) and principal component analysis(PCA), and imported to an artificial neural network(NN) model for prediction. With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology, MI is found to be more appropriate to provide effective input variables than PCA. Although the three factors regarding ship characteristics are highly correlated, it is recommended to input all of them to the NN model.The accuracy of predicting aft spring line force attains as high as 91.2%. The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors.