“深水地平线”事故深海溢油输移扩散的数值模拟

为深入认识深海溢油输运过程和提高深海溢油事故的应急响应能力,文章以2010年墨西哥湾“深水地平线”事故为例,采用深海溢油输移扩散模型,以三维流场和海面风场为主要环境动力,数值模拟溢油深海泄漏后的浮射扩散、水体中输移扩散以及在风场和流场共同作用下在海面上输移扩散的全过程,同时模拟实施海底消油剂喷注处理措施后溢油输移轨迹和扩散范围的变化。研究结果表明：数值模拟结果与相关报道的悬浮油带实际观测结果以及美国国家海洋与大气管理局的海水异常遥感监测结果总体相符,可为更加全面和精细的深海溢油输移扩散数值模拟研究奠定良好的基础。     

海上溢油粒子追踪预测模型中的两种数值方法比较

在海上溢油粒子追踪预测模型中,关键的是对拉格朗日微分方程的求解。本文首先通过数值实验比较了欧拉法和龙格-库塔法求解拉格朗日溢油轨迹微分方程的优劣,然后将其应用到2005年4月3日发生在大连附近的“ARTEAGA”油轮溢油事故的油膜粒子追踪模型中。数值实验和应用结果表明,在近岸不均匀流场下,用龙格-库塔方法解拉格朗日油粒子微分方程比用欧拉法求解精度高,用龙格-库塔方法模拟“ARTEAGA”油轮轨迹及其扩散范围与实际观测更为接近,而用欧拉法模拟溢油扩散的面积偏大。     

基于随机情景模拟的船舶溢油危害后果量化评价——以台湾海峡北部水域为例

为克服典型情景模拟法的缺陷,综合考虑船舶溢油发生的随机性、海洋水动力和风场的不确定性以及环境资源的敏感性,提出基于随机情景模拟的船舶溢油危害后果定量评价方法。通过随机情景模拟和网格化统计得到敏感区的溢油污染概率和最快到达时间,结合环境敏感指数和溢油量等参数计算综合溢油危害指数,并将其作为溢油危害后果定量评价指标。结果表明:台湾海峡北部水域不同季节发生船舶溢油的危害后果大小依次为夏季(27.8)秋季(25.5)春季(21.1)冬季(16.2),夏季溢油事故对牛山岛保护区的污染概率和危害后果相对最大(P=60%,Ck=41.2),达到较高级别;其他季节东甲列岛保护区的溢油污染概率和危害指数均为最高。随机情景模拟能够弥补事故情景,为评价船舶溢油危害后果风险提供一种新方法。     

同化技术在渤海溢油应急预报系统中的应用

溢油应急预报对溢油事故现场处理具有重要指导意义。国内外已开展大量溢油数值预报技术研究,但由于各类误差的引入(尤其风和流数值预报误差的引入)以及模型本身的不完善等各种原因导致溢油数值预报无法满足日益提高的溢油预报精度需求。随着现场观测技术和监测水平的提高,如何充分利用实时观测数据提高业务化溢油应急预报精度,并满足应急预报迅速快捷的要求,成为目前业务化溢油应急预报的首要问题。国家海洋环境预报中心于2008年实现了渤海溢油业务化预报系统的建立和业务化应用,本文针对当前渤海溢油业务化应急预报中存在的现实问题,利用已有渤海海上5个石油平台从2010年1月至2011年2月的风场观测数据,初步开展最优插值方法(optimal interpolation assimilation method,OI)同化技术在国家海洋环境预报中心渤海溢油应急预报系统风场订正的应用研究。本文采用交错订正方法,确定了OI同化技术中相关尺度因子的选取,从而实现在这5个观测站地理分布情况下,OI同化技术应用中参数的最优化,之后在理想实验和实际案例的应用中,该同化方法明显提高渤海溢油预报精度。本文为如何进一步利用同化方法迅速快捷地实现溢油应急预报精度的提高提供了一定研究基础。     

渤海湾海面溢油数值计算

采用不规则三角形网格的分布杂交法，建立渤海湾潮流数值模型，在此基础上建立溢油飘移模型，计算在不同风场和潮流场的共同作用下，溢油飘移的输移轨迹、可能抵岸的时间、地点及残留量等。     

蓬莱19-3 油田事故溢油数值模拟

利用FVCOM(Finite-volume coastal ocean numerical model)数值模型和MM5风场预报模式,在对渤海海域水动力场进行数值模拟的基础上,基于"油粒子"的欧拉-拉格朗日跟踪法和随机走动原理,并考虑风对溢油油膜漂移扩散的直接作用,建立了海洋溢油油膜漂移轨迹和扩散的数值预测模型。利用建立的模型对2011年6月蓬莱19-3油田事故溢油进行了数值模拟,模拟结果与RADARSAT卫星遥感监测数据相吻合。研究结果表明:在渤海中部地区夏季事故溢油模拟预测中,风漂移因子取0.024最为合理,模型可用于渤海蓬莱19-3油田附近事故溢油轨迹和扩散的快速预报,从而为该区域的溢油事故应急响应提供科学依据。     

Numerical Study of the Oil Spill Trajectory in Bohai Sea,China

Sixteen numerical experiments are conducted using the General NOAA Operational Modeling Environment (GNOME) to numerically study the oil spill trajectory in the Bohai Sea, China, for the purpose of providing information for the oil cleanup and disaster mitigation. High resolution coastline and topography data investigated by China's 908 Program are used to accurately describe the regional geographic feature of the Bohai Sea. Currents and waves required by GNOME are generated by the Hybrid Coordinate Ocean Model and Simulating Wave Nearshore, respectively. Experimental results show that oil spilled in different seasons and at different locations will result in completely different spilling trajectories, traveling distances, and polluting areas. Knowing that information comprehensively is vital to pollution control. Another important goal of this work is to provide users with simulated oil spill trajectory information through China Digital Ocean Prototype System (CDOPS), a grand marine information platform for managing, displaying, and public sharing of the data investigated by the China 908 Program. Users can obtain the trajectory, spreading, and potential impact area of the spilled oil through the oil spill module in CDOPS.     

A physical approach for the observation of oil spills in SAR images

In this paper, a physical approach to support oil spills observation over synthetic aperture radar (SAR) images is presented. Electromagnetic model is based on an enhanced damping model that takes into account oil viscoelastic properties and wind speed. As a matter of fact, a multisensor approach is considered and a constant false alarm rate (CFAR) filter is used to minimize speckle effect. A set of experiments is presented and discussed. They show that oil spill processing is effective over single-look SAR images using mean input data.     

Adaptive input–output feedback linearizing yaw plane control of BAUV using dorsal fins

This paper presents the design of an adaptive input–output feedback linearizing dorsal fin control system for the yaw plane control of low-speed bio-robotic autonomous underwater vehicles (BAUVs). The control forces are generated by cambering two dorsal fins mounted in the vertical plane on either side of the vehicle. The BAUV model includes nonlinear hydrodynamics, and it is assumed that its hydrodynamic coefficients as well as the physical parameters are not known. For the purpose of design, a linear combination of the yaw angle tracking error and its derivative and integral is chosen as the controlled output variable. An adaptive input–output feedback linearizing control law is derived for the trajectory control of the yaw angle. Unlike indirect adaptive control, here the controller gains are directly tuned. The stability of the zero dynamics is examined. Simulation results are presented for tracking exponential and sinusoidal yaw angle trajectories and for turning maneuvers, and it is shown that the adaptive control system accomplishes precise yaw angle control of the BAUV using dorsal fins in spite of the nonlinearity and large uncertainties in the system parameters.     

Projecting uncertainty onto marine megafauna trajectories

In this study, a method is proposed for estimating the uncertainty of a Lagrangian pathway calculated from an undersampled ocean surface velocity field. The primary motivation and application for this method is the differentiation between active and passive movements for sea turtles whose trajectories are observed with satellite telemetry. Synthetic trajectories are launched within a reconstructed surface velocity field and integrated forward in time to produce likely trajectories of an actual turtle or drifter. Uncertainties in both the initial conditions at launch and the velocity field along the trajectory are used to yield an envelope of possible synthetic trajectories for each actual trajectory. The juxtaposition of the actual trajectory with the resulting cloud of synthetic trajectories provides a means to distinguish between active and passive movements of the turtle. The uncertainty estimates provided by this model may lead to improvements in our understanding of where and when turtles are engaged in specific behaviors (i.e. migration vs. foraging)—for which potential management efforts may vary accordingly.     

胶州湾海面溢油轨迹的数值模拟

对胶州湾湾口两侧——团岛和薛家岛 1个月的潮位观测资料进行调和分析 ,得出湾口两侧 M2 ,S2 ,O1,K1分潮的调和常数 ,进而建立了胶州湾内的预报潮流场。考虑风场对海面溢油的影响 ,给出了预测胶州湾溢油飘移轨迹的数值模型 ,对 1 983年 1 1月 2 5日“东方大使”号油轮在中砂礁触礁后的溢油的油膜漂移轨迹进行了数值模拟 ,结果与事故后观测到的结果相符 ,说明了数值模拟的正确性     

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.     

Bohai Sea oil spill model: a numerical case study

An operational Bohai Sea oil spill serving module (BSOSSM) that can provide users with trajectory and movement information of the released oil is developed for the purpose of informing mitigation of oil spill incidents in the Bohai Sea, China. BSOSSM is one of the serving modules that had been integrated in China digital ocean prototype system, a marine information platform for managing, displaying and disseminating all the data investigated by China 908 Program. The oil spill trajectory is calculated by an oil spill model (OSM), which serves as a component in BSOSSM. The impacts of wind, current, as well as Stokes drift on oil spill trajectory are studied by sensitivity experiments conducted using OSM. Simulation results indicate that wind forcing is the most important factor in controlling the oil trajectory at the sea surface in Bohai Sea, whereas current and Stokes drift play relatively less important roles. However, because the direction of waves generally follows that of the wind, Stokes drift does lead to an increase in oil drift and spreading velocity. Case studies of the Penglai 19-3 oil spill incident (June 2011) and Xingang oil spill (April 2005) demonstrate that OSM can generally reproduce the oil spreading, and is therefore capable of supporting the emergency response of future oil spills in the Bohai Sea through BSOSSM.     

基于复contourlet域隐马尔科夫树模型的海面溢油合成孔径雷达图像相干斑抑制

海面溢油SAR图像中的相干斑噪声严重影响了后续的图像分割、特征提取和分类.为了更有效地抑制海面溢油SAR图像相干斑,文中提出了一种基于复contourlet域隐马尔科夫树模型的海面溢油SAR图像相干斑抑制方法.首先对观测图像取对数并进行复contourlet变换;然后在复contourlet域中用隐马尔科夫树模型对相邻尺度间的带通方向子带系数进行建模,并依据贝叶斯最小均方误差准则估计无噪系数;最后进行逆复contourlet变换和指数变换,得到相干斑抑制后的图像.大量实验结果表明,与Lee、Kuan、Frost及Gamma Map等4种经典滤波方法以及小波域和contourlet域隐马尔科夫树模型方法相比,文中方法从主观视觉和客观定量评价两方面来看综合性能更为优越,是一种行之有效的SAR遥感图像海面溢油检测的预处理方法.     

State-dependent Riccati equation-based robust dive plane control of AUV with control constraints

The paper treats the question of suboptimal dive plane control of autonomous underwater vehicles (AUVs) using the state-dependent Riccati equation (SDRE) technique. The SDRE method provides an effective mean of designing nonlinear control systems for minimum as well as nonminimum phase AUV models. It is assumed that the hydrodynamic parameters of the nonlinear vehicle model are imprecisely known, and in order to obtain a practical design, a hard constraint on control fin deflection is imposed. The problem of depth control is treated as a robust nonlinear output (depth) regulation problem with constant disturbance and reference exogenous signals. As such an internal model of first-order fed by the tracking error is constructed. A quadratic performance index is chosen for optimization and the algebraic Riccati equation is solved to obtain a suboptimal control law for the model with unconstrained input. For the design of model with fin angle constraints, a slack variable is introduced to transform the constrained control input problem into an unconstrained problem, and a suboptimal control law is designed for the augmented system using a modified performance index. Using the center manifold theorem, it is shown that in the closed-loop system, the system trajectories are regulated to a manifold (called output zeroing manifold) on which the depth tracking error is zero and the equilibrium state is asymptotically stable. Simulation results are presented which show that effective depth control is accomplished in spite of the uncertainties in the system parameters and control fin deflection constraints.     

渤海结冰海区溢油行为数值模拟

基于溢与海冰,水,大气的相互作用原理,建立了渤海结冰海区溢油行为数据模式,该模式模拟子结冰海区中油的输运过程,与国外有关模式相比更加重视冰场和冰速场的作用。海冰及潮流模式及采被实况检验过的渤海海冰数值预报模式。     

一定厚度的油膜对海浪的抑制模型研究

本文结合海浪谱的作用量平衡方程,给出了一定厚度油膜对海浪的抑制模型,该模型不仅与油膜的物理参数有关,而且与环境参数有关。文中开展了抑制比对这些参数的敏感性分析,数值模拟结果表明:动力学粘性,表面/界面弹性、油膜厚度、油膜覆盖率等参数对抑制比的影响较大,但油膜的其他物理学参数如:密度、表面/界面张力,表面/界面粘度几乎对抑制比不产生影响;而且,文中还发现风速和风向对抑制比的影响较大。一般情况下,油膜对雷达回波信号的抑制,只需考虑油膜对海浪的抑制作用;但对厚乳化溢油而言,还应考虑到介电常数的减小对雷达回波信号的抑制。将本文建立的抑制比模型所得结果与墨西哥湾溢油事故期间的15景ENVISAR ASAR影像所得抑制比结果进行了比较。     

三维海洋溢油预测模型的建立(英文)

提出一个海洋溢油三维物理归宿和输运的动力学综合模型。该模型包含了一系列的数学公式来描述对流、湍扩散、表面扩展、铅直扩散、乳化和蒸发过程。每一公式的建立是独立的并且与相关过程、环境和其它参数相联系。该模型需要输入流场作为输运的媒介 ,这可从感兴趣区域的三维潮和风驱动的流体动力学模型获得。模型用来预测和后报溢油在海洋环境中的归宿和输移 ,可为溢油应急反应和环境影响评价服务。     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.     

Integrating Uncertainty Theories with GIS for Modeling Coastal Hazards of Climate Change

Prediction of coastal hazards due to climate change is fraught with uncertainty that stems from complexity of coastal systems, estimation of sea level rise, and limitation of available data. In-depth research on coastal modeling is hampered by lack of techniques for handling uncertainty, and the available commercial geographical information systems (GIS) packages have only limited capability of handling uncertain information. Therefore, integrating uncertainty theory with GIS is of practical and theoretical significance. This article presents a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty. Coastal hazard is modeled as the combined effects of sea-level induced recession and storm erosion, using grid modeling techniques. The method is described with a case study of Fingal Bay Beach, SE Australia, for which predicted responses to an IPCC standard sea-level rise of 0.86 m and superimposed storm erosion averaged 12 m and 90 m, respectively, with analysis of uncertainty yielding maximum of 52 m and 120 m, respectively. Paradoxically, output uncertainty reduces slightly with simulated increase in random error in the digital elevation model (DEM). This trend implies that the magnitude of modeled uncertainty is not necessarily increased with the uncertainties in the input parameters. Built as a generic tool, the model can be used not only to predict different scenarios of coastal hazard under uncertainties for coastal management, but is also applicable to other fields that involve predictive modeling under uncertainty.