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This article presents an iterative six-step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.

Nowadays, LNG Import Terminals (where the storage and regasification process is conducted) are mostly onshore; the construction of these terminals is costly and many adaptations are necessary to abide by environmental and safety laws. Moreover, an accident in one of these plants might produce considerable impact in neighboring areas and population; this risk may be even worse due to the possibility of a terrorist attack. Under this perspective, a discussion is conducted about a vessel known as FSRU (Floating Storage and Regasification Unit), which is a storage and regasification offshore unit, that can work miles away from de coast and, owing to this, can be viewed as an option for LNG storage and regasification facilities. The goal is to develop a method for using Bayesian Networks in the Risk Analysis of Regasification System of the FSRU, which will convert Fault Trees (FT) into Bayesian Networks (BN) providing more accurate data. Using BN is possible to represent uncertain knowledge and local conditional dependencies. In addition, FT models the failure modes as independent and binary events while BN may model a larger number of states. It is worth noting that BN does not require the determination of cut sets; however, given a failure, it is capable of providing the probability of each possible cut set. This method will provide information to define, in a future study, a maintenance plan based on the Reliability Centered Maintenance. The results intend to clarify the applicability of BN on risk assessment and might improve the risk analysis of a Regasification System FSRU.

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17Forecasting the behaviour of a flammable or toxic cloud is a critical challenge in quantitative risk 18 analysis. Recent literature shows that empirical and integral models are unable to model complex 19 dispersion scenarios, like those occurring in semi-confined spaces or with the presence of physical 20 barriers. Although CFD simulators are promising tools in this regard, they still need to be fully 21 validated with comprehensive datasets coming from experimental campaigns designed ad-hoc. In this 22paper, we present an experimental campaign carried out by a joint venture between University of São 23Paulo and Universitat Politècnica de Catalunya to investigate CFD tools performance when used to 24 analyse clouds dispersion. The experiments consisted on propane cloud dispersion field tests 25 (unobstructed and with the presence of a fence obstructing the flow) of releases up to 0.5 kg/s of 40 s 26 of duration in a discharge area of 700 m 2 . We provide a full 1-s averaged propane concentration 27 evolution dataset of two experiments, extracted from 29 points located at different positions within the 28 cloud, with which we have tested FLACS® CFD-software performance. FLACS reproduces 29 successfully the presence of complex geometry, showing realistic concentration decreases due to cloud 30 dispersion obstruction by the existence of a fence. However, simulated clouds have not represented the 31 whole complex accumulation dynamics due to wind variation. 32 2 Keywords: consequence analysis, propane, field tests, computational fluid dynamics, FLACS 1 software. 2 INTRODUCTION3 Dispersion of hazardous gas releases occurring in transportation or storage installations represent a 4 major threat to health and environment. Therefore, forecasting the behaviour of a flammable or toxic 5cloud is a critical challenge in quantitative risk analysis. Cloud dispersion is often analysed using 6Gaussian and integral models that usually provide reliable and fast results for dispersions in simple 7 scenarios, i.e. unobstructed, over flat terrain; however, these models show limitations when applied to 8 study dispersions over terrains with any degree of complexity, like in offshore production units, 9 refineries or industrial plants. Obstacles present in the cloud scattering path interact with the gas flow 10 and generate turbulence causing an important effect in the cloud dispersion. The impact of the 11 geometry on dispersion has to be necessarily evaluated to perform an accurate dispersion analysis and 12 simple models are not able to tackle it. The latter treat terrain complexities by means of a surface 13 roughness parameter, which becomes a very imprecise and unrealistic approximation when modelling 14 dispersion with complex local geometries. 15The interest of Computational Fluid Dynamics (CFD) codes to solve complex flow related problems 16 has been extended within the scientific community, together with the increase on computational 17 capacity. One of the areas where CFD has already shown its potential is in ris...