Ângelo P. Teixeira

Ultimate strength of plates with random fields of corrosion

This paper presents a study of the collapse strength of corroded plates with random spatial distributions of corroded thicknesses. As an alternative to the uniform reduction of plate thickness due to corrosion or a localized area of reduced thickness, the spatial distributions of the thickness of the corroded plate represented by stochastic simulations of random fields are considered. A non-linear time dependent corrosion model is used to define the probabilistic characteristics of the random fields based on corrosion data measured in plate elements at different locations of several bulk carriers. The random fields of corrosion are discretized and the collapse strength of the plate is then assessed by non-linear finite element analysis. The importance of the spatial representation of the corrosion by random fields as an alternative to the traditional approach, based on a uniform reduction of the plate thickness, is demonstrated.

Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks

This article develops a Bayesian belief network model for the prediction of accident consequences in the Tianjin port. The study starts with a statistical analysis of historical accident data of six years from 2008 to 2013. Then a Bayesian belief network is constructed to express the dependencies between the indicator variables and accident consequences. The statistics and expert knowledge are synthesized in the Bayesian belief network model to obtain the probability distribution of the consequences. By a sensitivity analysis, several indicator variables that have influence on the consequences are identified, including navigational area, ship type and time of the day. The results indicate that the consequences are most sensitive to the position where the accidents occurred, followed by time of day and ship length. The results also reflect that the navigational risk of the Tianjin port is at the acceptable level, despite that there is more room of improvement. These results can be used by the Maritime Safety Administration to take effective measures to enhance maritime safety in the Tianjin port.

Modelling and simulation of the operation and maintenance of offshore wind turbines

The offshore environment limits the accessibility to the wind turbines and subjects them to faster degradation processes than in onshore. Thus, operation and maintenance is more challenging and costly and represents a considerable share of the cost of energy. It is therefore important to identify which factors most influence the turbines’ performance, namely, the availability, overall cost and revenues, so that actions can be taken to minimize their effect. This article addresses such issues by presenting a parametric study on how the variation of failure and repair models, vessels logistic times, weather windows and waiting times affect a wind turbine performance. Offshore failure models/data were not usually available on the public domain, being obtained herein from onshore ones using an empirical approach based on stress factors for mechanical systems. The baseline model results from the optimization of an operation and maintenance strategy based on corrective maintenance replacements and imperfect age-based preventive maintenance repairs. Generalized stochastic Petri nets with predicates coupled with Monte Carlo simulation are used for modelling and simulation. Results are discussed.

Kriging response surface reliability analysis of a ship-stiffened plate with initial imperfections

The use of structural reliability methods with implicit limit state functions (LSFs) shows the increasing demand for efficient stochastic analysis tools, because the structural behaviour predictions are often obtained by finite element analysis. All stochastic mechanics problems can be solved by Monte Carlo simulation method, nevertheless, in most cases, at a prohibitively high computational cost. Several approximations can be achieved using first-order reliability method (FORM) and second-order reliability method and response surface methods. In this paper, a method that combines the FORM and Kriging interpolation models, as response surface, is proposed. The prediction accuracy of the Kriging response surface obtained from different sampling techniques is assessed, and the failure probability estimates calculated by the FORM using the classical second-order polynomial regression models and the Kriging interpolation models as surrogates of nonlinear LSFs are compared. The usefulness and efficiency of the reliability analysis using the Kriging response surface are demonstrated on the basis of existing results available in the literature and with an application problem of a stiffened plate structure with initial imperfections.

Operation and Maintenance of Floating Offshore Wind Turbines

This chapter starts by shortly addressing the statistics of accidents and component failures of wind turbine structures based on a comprehensive dataset publicly available. The distribution of the types of offshore wind turbine structures installed in European waters is given. The operation and maintenance of fixed structures foundations is discussed. Then, the failure data of main subassemblies of wind turbines are presented and discussed, followed by a description of available and important condition monitoring systems, techniques and methods for operation and maintenance of wind turbines. Finally, the knowledge on modelling, simulation and optimization of operation and maintenance actions of fixed offshore wind turbines is discussed as a basis for the application in the operation and maintenance of floating offshore wind turbines.

Maintenance Planning of an Offshore Wind Turbine Using Stochastic Petri Nets With Predicates

Operation and maintenance (O&M) activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing O&M actions in industrial systems. Generalized stochastic Petri Nets (GSPN) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of O&M activities of an offshore wind turbine. The merits of GSPN in modeling complex, multi-state and multi-component systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays and costs are included in the model. The weather windows for accessing the wind turbine are also modeled. Corrective maintenance based on replacements and age imperfect preventive maintenance are modeled and compared in terms of the wind turbine’s performance (e.g. availability and loss production) and of the O&M costs.Copyright

Modeling, simulation and optimization of maintenance cost aspects on multi-unit systems by stochastic Petri nets with predicates

The paper addresses repairable multi-unit systems with a series–parallel configuration for which maintenance strategies are modeled by generalized stochastic Petri nets (GSPN) with predicates coupled with Monte Carlo simulation. Four maintenance strategies consisting of basic periodic preventive and corrective maintenance, and both combined with opportunistic maintenance (OM) strategies, are considered. Failure and repair distributions of the system components are independent, and repairs are considered to be perfect. Times to failure of degraded components follow a Weibull distribution with increasing failure rate over time. The maintenance strategies are optimized so as to minimize the total maintenance costs of the system while maximizing availability. A comparison is drawn between OM and non-OM. The aim is to show that GSPN with predicates, in combination with Monte Carlo simulation, is a powerful, flexible, efficient, and intuitive approach for modeling and optimizing practical maintenance strategies on multi-unit complex systems, modeling the dynamic behavior resulting from the interaction between system components and economic dependencies. The merits and advantages of GSPN coupled with Monte Carlo simulation are enhanced relative to other, analytical, approaches.