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Hazard identification and probabilistic scenario selection for ship-ship? Collision accidents
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In collision risk-based design frameworks it is necessary to accurately define and select a set of credible scenarios to be used in the quantitative assessment and management of the collision risk between two ships. Prescriptive solutions and empirical knowledge are commonly used in current maritime industries, but are often insufficient for innovation because they can result in unfavourable design loads and may not address all circumstances of accidents involved. In this study, an innovative method using probabilistic approaches is proposed to identify relevant groups of ship-ship collision accident scenarios that collectively represent all possible scenarios. Ship-ship collision accidents and near-misses recently occurred worldwide are collated for the period of 21 years during 1991 to 2012. Collision scenarios are then described using a set of parameters that are treated individually as random variables and analysed by statistical methods to define the ranges and variability to formulate the probability density distribution for each scenario. As the consideration of all scenarios would not be practical, a sampling technique is applied to select a certain number of prospective collision scenarios. Applied examples for different types of vessels are presented to demonstrate the applicability of the method.
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... ( ) 0, 1, 2, , (1) and when J or K is not zero, it is called a constrained optimal problem. S , and energy absorption under impact loading were selected to form a multi-objective function. ...
The application of corrugated steel sandwich panels on ships requires excellent structural performance in impact resistance, which is often achieved by increasing the weight without giving full play to the characteristics of the structure. Considering the mechanical properties of sandwich panels under static and impact loading, a multi-objective optimal method based on a back-propagation (BP) neural network and a genetic algorithm developed in MATLAB is proposed herein. The evaluation criteria for this method included structural mass, static and dynamic stress, static and dynamic deformation, and energy absorption. Before optimization, representative sample points were obtained through numerical simulation calculations. Then, the functional relationship between the design and output variables was generated using the BP neural network. Finally, a standard genetic algorithm (SGA) and an adaptive genetic algorithm (AGA) were used for multi-objective optimization analysis with the established function to obtain the best result. Through this study, a new design concept with high efficiency and reliability was developed to determine the structural parameters that provide the best impact resistance using limited sample points.
The objectives of the present paper are to identify the hazard of ship grounding; where a ship runs on a rock with a forward speed, and to select a set of credible scenarios with a limited number that can still represent all possible situations of the accidents. For this purpose, the statistics of ship grounding accidents recorded by authorities for the period of 46 years during 1970–2016 are collated. An extensive analysis is undertaken to examine the statistical characteristics in association with random variables influencing the consequence of grounding. A total of six parameters, namely ship's forward speed, ship's trim angle, rock tip eccentricity, rock length, rock width and rock height are considered as random variables where the displacement or mass of the grounded ship is fixed.
Each of the random variables is then formulated with a probability density function. A sampling technique is
applied to the probabilistic selection of the grounding scenarios which are to be used for the consequence analysis within the framework of quantitative risk assessment. Important insights developed from the present study are discussed. Details of the analyses are documented.
Dropped objects is one of the most hazardous accidents of floating production storage offloading (FPSO) due to the potentially severe consequences, therefore, the quantitative calculation of dropped objects impact risk, which is featured with uncertainties, is essential. Monte Carlo Simulation (MCS) is a method for this intention with flexibility and efficiency. However, wide calculating resources are needed for MCS-based accident analysis, especially when multiple random variables are concerned. Moreover, dropped objects collision damage models are perplexed and usually pronounced by the Finite Element Method (FEM), which is of a computational complexity. To address this issue, this paper considered a combined methodology: the Artificial Neural Network (ANN) adjusted by genetic algorithm (GA) is united with MCS to analyze dropped objects collision failure probability quantitatively. A time-dependent progressive simulation is employed to forecast the structural response caused by dropped objects collision. Then the ANN-GA is trained based upon the collision data and used as an alternative for the computational FEM performs with the MCS methodology. The risk level for the dropped objects collision of FPSO is assessed by comparing the proposed method and the DNV rules, and a qualitative uncertainty analysis is also conducted in this quantitative failure analysis.
Ship collision accidents are rare events but pose huge threat to human lives, assets, and the environment. Many researchers have sought for effective models that compute ship stochastic response during collisions by considering the variability of ship collision scenario parameters. However, the existing models were limited by the capability of the collision computational models and did not completely capture collision scenario, and material and geometric uncertainties. In this paper, a novel framework to performance characterisation of ships in collision involving a variety of striking ships is developed, by characterising the structural consequences with efficient response models. A double-hull oil carrier is chosen as the struck ship to demonstrate the applicability of the proposed framework. Response surface techniques are employed to generate the most probable input design sets which are used to sample an automated finite element tool to compute the chosen structural consequences. The resulting predictor-response relationships are fitted with suitable surrogate models to probabilistically characterise the struck ship damage under collisions. As demonstrated in this paper, such models are extremely useful to reduce the computational complexity in obtaining probabilistic design measures for ship structures. The proposed probabilistic approach is also combined with available collision frequency models from literature to demonstrate the risk tolerance computations.
As the total world trade of oil by tankers grow, the potential risk to the marine environment increases. When oil tankers are
involved in accidents (e.g., collision or grounding), a consequence of the resulting damage could be the release of crude
oil or petroleum products into the sea. The aim of the present study is to investigate the environmental consequences of the
involnement of oil tankers in collision. Using probabilistic approaches, credible scenarios of ship–ship collision are selected
to create a representative sample of the most possible ones. The LS-DYNA non-linear finite element method is used to
predict the resulting damage or opening associated with the individual scenarios. The environmental consequences are then
estimated by calculation of the amount of oil spilled in each scenario. In addition, the potential damage to the environment
is presented in terms of monetary units that can be understandable to all stakeholders.
In recent decades, the safety of ships at sea has become a major concern of the global maritime industries. Ships are rarely subject to severe accidents during their life cycle. Collision is one of the most hazardous accidents, with potentially serious consequences such as the loss of human life, structural damage and environmental damage, especially if large tankers, LNG and/or nuclear-powered vessels are involved. This study presents a Quantitative Risk Assessment (QRA) for double hull oil tankers that have collided with different types of ships. The methodology used to perform the QRA is based on the International Maritime Organization's (IMO) definition of a Formal Safety Assessment (FSA). Using probabilistic approaches, ship-ship collision scenarios are randomly selected to create a representative sample of all possible scenarios. The collision frequency is then calculated for each scenario. As this is a virtual experiment, the LS-DYNA nonlinear finite element method (NLFEM) is used to predict the structural consequences of each scenario selected. In addition, the environmental consequences are estimated by calculating the size of each scenario's oil spill. To assess the economic consequences, the property and environmental damages are calculated in terms of monetary units. The total risk is then calculated as the sum of the resultant structural and environmental damages. Exceedance curves are established that can be used to define the collision design loads in association with various design criteria.
The collision dynamics model is a vital part in maritime risk analysis. Different models have been introduced since Minorsky first presented collision dynamics model. Lately, increased computing capaci-ty has led to development of more sophisticated models. Although the dynamics of ship collisions have been studied and understanding on the affecting factors is increased, there are many assumptions required to com-plete the analysis. The uncertainty in the dynamic parameters due to assumptions is not often considered.
In this paper a case study is conducted to show how input models for dynamic parameters affect the results of collision energy calculations and thus probability of an oil spill. The released deformation energy in collision is estimated by the means of the analytical collision dynamics model Zhang presented in his PhD thesis. The case study concerns the sea area between Helsinki and Tallinn where a crossing of two densely trafficked wa-terways is located. Actual traffic data is utilized to obtain realistic encounter scenarios by means of Monte Carlo simulation. Applicability of the compared assumptions is discussed based on the findings of the case study.
The aims of this study are to obtain the experimental data on the structural behavior of 2 types of side structures, standard VLCC and VLCC with additional side stringers, during ship-ship collision, and to investigate the internal mechanics related to the structural deformation of the struck ship, Two quasi-static collision tests were carried out on the models of side structures, which are a part of the double hull. As the bow of the striking ship is assumed to be rigid, the energy absorption capacity of 2 designs was studied. The contribution of various structural components, such as outer/inner shell plating, web frame, side stringer, etc, is discussed. Especially, the distribution of strain on the side shell plating during collision was collected in order to verify the usefulness of the numerical analysis by FEA codes.
Current regulatory methods for predicting tanker performance in accidental grounding and collision do not properly consider environmental risk or account for variations in ship structural design. This paper investigates alternatives for a tanker environmental risk index, and proposes a performance assessment methodology which includes methods for estimating damage extents based on ship structural design and accident scenario probabilities. This methodology is used to assess the environmental performance of representative single hull, double hull and mid-deck tankers.
In this paper, an advanced method for the development of an empirical model to predict time-dependent corrosion wastage is proposed. It is well recognised that the statistical scatter of corrosion wastage at any exposure time is generally very wide, and the reliability of a corrosion model thus relies on how accurately such scatter is measured. In the proposed method, the statistical scatter of corrosion wastage at any exposure time is analysed in a refined manner and formulated using the Weibull function. Both the shape and scale parameters of the Weibull function are determined as a function of time through the curve fitting of a corrosion measurement database. An application of the proposed method’s use in the development of an empirical model for the seawater ballast tank structures of aged ships is presented. Its ability to generate an empirical corrosion model is confirmed, thus making it possible to predict time-dependent corrosion wastage more accurately.