The leak of flammable and/or toxic substances is great matter of concern to industry sectors that work with such products. Given the high risk associated with the possible consequences of these types of leakage, such as fires, explosions and environmental contamination, it becomes extremely important to evaluate the behavior of these substances when released directly into the atmosphere or in confined environments. Several methods are used to model the consequences of these releases; there is no single method for model such consequences. Usually integral and Gaussian models are used to modeling releases in open environments, without obstacles; and CFD tools (Computational Fluid Dynamics) are used to simulate releases in confined or with some degree of obstruction environments.
The water cargo transportation is historically marked by the need for effective planning and mobilization of a lot of resources to ensure integrity, efficiency and safety. Several risks are associated with the operation both on board ships and in terminals, according to the major dimensions involved and also the loaded content that may contain highly hazardous aggregate. This line of research aims to assess vessels and terminals involved in cargo transportation in order to assess the associated risks and propose effective security measures.
Bayesian Networks are graphical models based on uncertainty, where the nodes represent random variables (discrete or continuous) and the arcs represent the direct connection between them. Initially developed for the improvement of artificial intelligence systems, Bayesian Networks have become an effective tool for risk analysis of complex systems, especially for efficiency in representing uncertainties and conditional probabilities.
Risk management in engineering projects is essential to the smooth running of the project and prevent undesirable consequences. However, such approach may result in changes in project configuration and tends to result in additional costs to ensure the tolerability of the risk associated with the project. Therefore, the efficiency with which these resources are allocated is of great importance in order to avoid unnecessary expenses and also to avoid that prevention measures have a negative impact on project performance, both in its design phase and during its operation.
Usually to evaluate the behavior of a physical system the creation of a representative conceptual model is essential. Therefore, one should assess the correlation between the mathematical models, experimental models and the real system, in order to ensure the reliability and validity of the results obtained. As part of the risk analysis, such assessment is fundamental and should be done with caution; since, the ramifications of any conceptual error can result in analytical inaccuracies and disaster both to life and to property and the environment.
Currently, the use of software is widespread to support human activities in many fields. In order to ensure the proper functioning of these sectors, it is essential that the software used to operate satisfactorily and with the fewest possible errors. Thus, it becomes important to manage risks associated with software targeting its development stage and their maintenance after its conception.
The increasing complexity of engineering systems requests maintenance processes increasingly elaborate in order to ensure uninterrupted operational conditions. Therefore, it is essential the management of specific procedures to ensure the success and efficiency in the maintenance tasks for which they were developed.
Faced with a recent history of nuclear accidents such as Chernobyl (1986) and Fukushima (2011), the reliability of facilities that work with nuclear power sources is constantly called into question by the authorities, as well as the consequences of a possible disaster being target of great concern on the part of society. Several methodologies that employ probabilistic calculation allow the safety assessment of these facilities, considering the configuration of the systems involved and the relationships between its components in order to map the main ways in which faults can occur and thus way to contribute to the development of mitigation measures. It is intended to meet the requirements of national and international standards of nuclear safety.
Offshore systems are generally related to the transportation and production of flammable and/or toxic substances, which have an inherent risk factor. With new scenarios such as oil exploration in the Pre-Salt layer, new concerns arise about the safe with which these activities are performed. Any negligence can cause damage to life and environmental or economic and/or financial losses. To ensure safe operations, it is essential to carry out the reliability analysis of vessels, platforms and equipment involved.
Evaluation of the physical components of an engineering system is an important stage of system reliability analysis; however, it alone does not cover all existing possibilities for failure. A significant proportion of accidents in various industries such as aeronautics, naval and chemistry, are associated with operators errors. Consider the human error contribution to the operation of complex systems involves techniques that have been improved over the past several decades and that are essential in the development of appropriate procedures for reliability enhancement.
Bayesian networks are probabilistic graphical models that relate random variables (discrete or continuous) from its conditional dependence. Initially developed in the 1980s to assist in obtaining information on Artificial Intelligence systems (AI), the Bayesian networks are currently used as a tool for the evaluation of complex systems. Therefore, its application in reliability analysis is of great interest due to its adaptable character to update data.
As well as several other sectors of the economy, the shipping industry generally behaves as a complex system. Accordingly, the dynamic systems technique allows an industry performance evaluation function of time, whereas cycles, delays and other elements characterized by the nonlinearity. In this line of research topics such as organizational learning curves, sustainability, influence of macroeconomic factors and the acquisition and disposal of vessels are assessed.
The development of the system dynamics technique allows a wide range of applications in industrial sectors. Thus, it is possible to perform the modeling of different types of markets from a direct organizational logic between flows and stocks, cyclical and time to understand all the behavior from the relationship between its parts.
