Coastal erosion and sea level rise: Implications for ocean beach and San Francisco's westside transport project

Abstract

One of the consequences of sea level rise resulting from the greenhouse effect is increased coastal erosion. This article discusses a model of erosion that can be used to estimate the response of beaches to sea level rise. The model is applied to Ocean Beach, California, with particular attention to the consequences of accelerated erosion for the San Francisco Westside Sewer Transport. Results obtained show that erosion produced by accelerated sea level rise could cause substantial damage to the structure. Large expenditures on beach nourishment will be required to protect the transport and the recreational value of the beach.     

基于偏好函数与深度信念网络的自动化码头AGV实时调度算法

为提高自动化集装箱码头岸桥作业效率、降低自动导引车(Automated Guided Vehicle,AGV)空载行驶距离,以偏好函数和深度信念网络(Deep Belief Network,DBN)为核心设计AGV实时调度算法,完成AGV任务分配指派,优化AGV作业任务序列。利用偏好函数为处于待分配状态的AGV筛选最优集装箱,产生训练样本并更新训练集,通过DBN实时更新集装箱的偏好函数,重复该过程直至集装箱作业完毕。算例分析表明:同两种现行调度规则对比,AGV空载距离和岸桥作业时间显著下降;与人工神经网络(Artificial Neural Network,ANN)相比,DBN可有效提高算法精度与求解效率;算法针对环境动态变化实时分配集装箱,为自动化码头提高效率和降低能耗提供依据。     

Onboard tuning of vessel seakeeping model parameters and sea state characteristics

It is essential for a safe and cost-efficient marine operation to improve the knowledge about the real-time onboard vessel conditions. This paper proposes a novel algorithm for simultaneous tuning of important vessel seakeeping model parameters and sea state characteristics based on onboard vessel motion measurements and available wave data. The proposed algorithm is fundamentally based on the unscented transformation and inspired by the scaled unscented Kalman filter, which is very computationally efficient for large dimensional and nonlinear problems. The algorithm is demonstrated by case studies based on numerical simulations, considering realistic sensor noises and wave data uncertainties. Both long-crested and short-crested wave conditions are considered in the case studies. The system state of the proposed tuning framework consists of a vessel state vector and a sea state vector. The tuning results reasonably approach the true values of the considered uncertain vessel parameters and sea state characteristics, with reduced uncertainties. The quantification of the system state uncertainties helps to close a critical gap towards achieving reliability-based marine operations.     

雷达跟踪目标的残差检测自适应多模型算法

提出了一种基于残差的自适应多模型算法，通过残差的大小来分配多模型中单个模型在整体估计中的权重。本算法无须机动检测，从理论上避免了延迟。通过Monte Carlo仿真表明本算法的有效性。     

基于多尺度随机动态模型的惯性器件数据融合估计

结合离散小波变换、动态系统理论及随机过程理论,建立了以尺度为变量的多尺度随机动态模型,并给出状态基于多尺度随机动态模型的多尺度递归数据融合算法,实现了在状态基于全局观测信息的优化估计值。该算法可以在无状态模型情况下进行数据融合,适用于难以获得或获得的状态模型不精确的情况。将此方法用于陀螺信号处理中,通过不同尺度下陀螺观测值的融合,陀螺信号的精度有明显的提高。仿真和实验均证明该算法是一种有效的数据融合算法。     

动态载荷识别的自适应延迟逆模型方法

为减小载荷识别问题对原系统先验知识的依赖,采用系统的自适应延迟逆模型识别时域载荷。采用自适应算法辨识延迟逆模型,代替了一般识别方法中的系统特性矩阵求逆过程,避免了病态问题。随后将工作状态下的响应作为逆模型的输入,则其输出就是时域载荷的延迟估计。通过对两端简支梁结构进行载荷识别的仿真研究,以及对双层隔振试验台架的试验研究,识别了稳态激励和瞬态激励,验证了该方法的有效性。该方法不需要了解系统的数学模型及参数,因此能够应用于工程实践中。     

基于改进深度学习算法的船舶柴油机故障诊断技术

随着数据挖掘技术的发展,深度置信网络(DBN)这类深度学习算法被越来越多运用到工程领域。在故障诊断领域,结合DBN强大的自适应特征提取和非线性映射能力,可以摆脱以往对专家经验的依赖。基于此,本文为有效地监测柴油机气缸运行状态,提出一种基于改进深度学习算法的船舶柴油机故障诊断技术。先将原始信号的频域形式输入DBN当中,采用蚱蜢优化算法(GOA)搜索DBN的最优参数组合,并建立起最佳的柴油机气缸故障诊断模型。经测试验证,本文提出的诊断模型能够准确识别柴油机气缸运行状态并进行故障诊断,诊断率可以达到99.5%以上,具有较好的工程实用价值。     

Epistemic-based investigation of the probability of hazard scenarios using Bayesian network for the lifting operation of floating objects

Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.     

基于模糊自适应IMM算法的机动目标跟踪方法

提出了一种模糊自适应IMM算法(FAIMM)自适应地设计IMM算法中的模型集.该方法根据加速度估计值,通过模糊推理获得所使用模型的后验概率,自适应地摒弃概率较小的模型,从而减少了模型数目,降低了算法的计算量.仿真结果表明,应用FAIMM算法跟踪目标,跟踪的精度、平稳性以及算法的收敛速度等都得到了改善.     

Real-time assessment and prediction on maritime risk state on the Arctic Route

ABSTRACT

Recently, the Arctic Route has become busier with the continuous melting of Arctic ice. However, navigation on the Arctic Route would be much more complex than in normal water as harsh environmental conditions, such as ice-covered water and scarce costal ports that may cause more uncertainty. Nowadays, with the rapid development of sensors on board, more related data has become available. Thus, implementing comprehensive Arctic maritime risk assessment is urgent and necessary in practice. This study proposes an Arctic maritime risk state assessment method including real-time risk state assessment and risk prediction. Specifically, real-time observation samples’ numerical risk state would be firstly obtained with projection pursuit method from 10 risk indicators. Due to the fuzzy uncertainty of single observation set, information diffusion would be applied to provide diffusion estimation on risk probability distribution in order to depict risk state precisely. Also, the accumulated distribution can be regarded as the risk prediction for next time slot and risk entropy is introduced to depict risk tendency directly. Case study based on ‘Yongsheng’ is conducted to demonstrate and verify the effectiveness of the proposed method. The findings can be useful for the operators and management on board during the Arctic voyage.     

Decadal sea level trends in the Bay of Biscay from tide gauges, GPS and TOPEX

Sea level time series derived from TOPEX altimeter and from tide gauge measurements in the Bay of Biscay (Eastern North Atlantic Ocean) are used to investigate the regional sea level rise. Altimetry sea level anomalies are computed from TOPEX measurements and resampled into fixed along track bins in order to obtain consistent records and to approach as close as possible to the coast. Tidal corrections are critical in the estimation of sea level trends; therefore an additional analysis has been carried out in order to identify and correct for residual tidal signals. The obtained mean sea level rise in the region is + 3.09 ± 0.21 mm/year over the period 1993–2002. Tide gauges have been corrected for vertical land motions by means of collocated GPS measurements. GPS-corrected tide gauges and nearby altimetry trends are found to be statistically consistent. The relationship with the atmospheric forcing is also investigated revealing that 15% of this sea level rise is attributed to the atmospheric pressure effects.     

基于SPH方法的连续式破冰数值模拟

根据海冰的力学性质,采用光滑粒子流体动力学(SPH)方法建立海冰本构模型,模拟冰锥实验并与实验结果对比,对冰材料模型准确性进行验证.利用显式动力分析软件LS-DYNA模拟破冰船连续式破冰,得到了海冰的损伤变形和破冰阻力,同时研究了船舶航速和海冰厚度等参数对冰载荷的影响.分析研究表明:应用SPH方法建立的海冰模型能够真实的反映海冰的力学性能,对连续式破冰过程实现较为准确的模拟,为船冰碰撞的数值模拟提供了新思路.     

An empirical model for estimating berth and sailing times of mega container ships

ABSTRACT

We provide two empirical models for calculating the sailing time and berth time of maritime container liner networks to effectively model the ambiguity associated with sea and port contingency for ex-ante decisions of fleet deployment and route planning. The models are based on recorded AIS data of 110 mega vessels including all the operating container mega vessels with a capacity of 16,000 TEU or more during the summer of 2015. The models are able to estimate the sailing time (with R² of 0.974) and the berth time (with R² of 0.895) without knowledge of any operational-level explanatory variables. The models are validated against the published East Asia–North Europe services. Moreover, the study reveals that vessel operators adopt different berthing and sailing strategies even under the same conditions.     

A risk assessment model with systematical uncertainty treatment for container shipping operations

ABSTRACT

The substantial adverse effects of risk factors on container shipping and logistics promoted a deep integration of risk analysis into the decision-making process. This paper aims to develop a well-grounded quantitative model to operational risk in a container shipping context. Considering uncertainty as a primary component of the risk concept, methods were employed in an inter-complementary manner to enable not only a sense of foreseeability but also a deeper look into the weaknesses of the knowledge base. The intersubjectivity of the input extraction process was supported by the Evidential Reasoning (ER) algorithm. Risks are then assessed based on a Fuzzy Rules Bayesian Network (FRBN) model with a 2-level parameter structure before meaningful interpretations can be derived through a new risk mapping approach. Besides an illustrative case study, the model was tested by sensitivity analysis and an examination of multiple validity claims.     

A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks

This work reports a new methodology for deriving monthly averages of temperature (T) and salinity (S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron (MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model. The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered, inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the Indian Ocean reveals that this approach can be extended to model global oceans.     

半潜式支持平台靠泊海洋生产平台碰撞概率建模

为有效解决深水半潜式支持平台靠泊海洋生产平台过程中存在的碰撞问题,从碰撞事故发生原因入手,借鉴挪威船级社(Det Norske Veritas,DNV)DNV-RP-107规范中的船与海洋平台碰撞概率模型和美国公路与运输协会(American Association of State Highway and Transportation Officials,AASHTO)《美国公路桥梁防船撞设计指南》中的船桥碰撞概率模型,将支持平台发生向前的过分偏移引起的碰撞场景分为漂移碰撞和动力碰撞2种,建立事件树,得到支持平台靠泊海洋生产平台碰撞概率模型。在此基础上,估算支持平台靠泊碰撞海洋生产平台的概率,由此评估碰撞风险,为半潜式支持平台靠泊碰撞海洋生产平台场景下的相关规范制定提供参考。     

高桩码头岸坡地震位移计算方法比较

在强震区高桩码头设计中,基于位移的设计准则允许码头岸坡震后出现一定的位移,验算码头岸坡地震位移是高桩码头抗震设计的重要内容之一。依托某强震区高桩码头,针对目前广泛使用的基于NEWMARK刚性滑块位移理论的经验公式进行比较分析,如MakdisiSeed公式、MartinQiu公式、Jibson公式、SaygiliRathje公式、Jonathan公式等;并采用有限差分软件FLAC 2D进行动力时程分析验证。结果表明,尽管不同经验公式的位移估算结果存在一定的差异,但总体符合Jonathan提出的概率分布模型,动力时程分析法在位移评估上具有一定的优势。     

Out of sight is not out of mind: Public opposition to ocean incineration

Abstract

During the early 1980s, ocean incineration emerged as one of the hottest issues in the field of hazardous waste management. Ocean incineration involves the thermal destruction of liquid hazardous wastes on specially designed ships and was promoted by the Environmental Protection Agency and powerful industry groups as part of the solution to liquid hazardous waste disposal. Attempts to adopt ocean incineration led to heated debate and apparent defeat of efforts to introduce this technology. The arguments advanced by proponents and opponents are examined. Success of environmental groups opposed to ocean incineration is explained with reference to technical, environmental, socio‐political, and legal factors. Ocean incineration, promoted as a solution to “not‐in‐my‐backyard”; opponents, instead ran afoul of region wide opposition by coastal residents for whom the sea is a common backyard.     

Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.     

State ocean policy initiatives in Florida

Abstract

A territorial sea comprising 6.7 million acres of offshore lands extending three geographic miles into the Atlantic Ocean three marine leagues (nine geographic miles) into the Gulf of Mexico makes Florida the second largest “oceanowning”; state in the contiguous United States. Florida's history, climate, economic development, and worldwide reputation have evolved from the state's relationship with the sea. The state is now becoming aware that aggressive management of its ocean resources is necessary to preserve the future of that relationship. The purpose of this article is to present some of Florida's recent legal and policy initiatives that have been directed toward better understanding and management of ocean resources and uses.