Compared to conventional marine systems, where the onboard crew can perform frequent and flexible maintenance, autonomous marine systems (AMS) only involve a limited (or, even no) crew during a voyage, and this challenges maintenance planning and execution. The current study identifies the relevant issues and proposes to solve these through developing a dynamic maintenance planning method for AMS. By considering economical dependencies among components, the study presents a dynamic grouping method to determine the optimum maintenance opportunities for AMS in the future. Stochastic dependencies of components are considered by using the Markov model. A multiphase Markov model is proposed for modeling stochastic dependencies between components where the limited and irregular maintenance opportunities are handled by the multiphase part of the model. A heuristic method is proposed to deal with the combinatorial challenge. To demonstrate the application of the proposed method, the maintenance planning of a cooling system of an autonomous ship is performed in a case study. To validate its performance, the proposed heuristic method is compared with existing 'short-sighted' methods for a selection of candidate groups for maintenance. In the validation, various scenarios with different component states and maintenance strategies are tested.

Since few or no human operators are directly involved in the operation of an autonomous marine system (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision making. The current study combines the system-theoretic process analysis (STPA) with Bayesian belief networks (BBNs) to develop online risk models for an AMS. Furthermore, fuzzy discretization is introduced to deal with evidence uncertainty. The proposed risk model can update the risk level as the operating conditions change, providing a basis for AMS supervisory risk control (SRC). A two-level SRC is proposed in this study. Using the operation of an autonomous underwater vehicle (AUV) under sea ice as an example, the current work presents an online risk model and a corresponding SRC system, focusing on the navigation hazards to the AUV and its potential loss. The results of simulation studies show that the model enables the AUV to be informed of the risk level and to make risk-based decisions accordingly, thereby improving its intelligence. The importance of the evidence uncertainty in online risk models and SRC is analyzed and discussed. The results and conclusions of this analysis can be adapted to other AMSs.

Considering that few or no human operators are directly involved in the operation of Autonomous Marine Systems (AMS), an online risk model is necessary to enhance the intelligence of the AMS, its situation awareness, and decision-making. The current study identifies the criteria for an online risk model for AMS, which can be used to assess its validity and effectiveness. Taking an under-ice Autonomous Underwater Vehicle (AUV) operation as an example, the current work investigates how different risk analysis methods, namely the Preliminary Hazard Analysis (PHA), the Systems Theoretic Process Analysis (STPA), and Procedural Hazard and Operability Analysis (HAZOP), contribute to fulfilling the different criteria for online risk modeling of AMS. The analysis results show that STPA can be considered a good basis for developing an online risk model due to its relatively good coverage of the identified evaluation criteria, especially its ability to handle the interaction between system and software failure. In addition, considering some shortcomings of using STPA and the changing role of human operators in the AMS operation, PHA and Procedural HAZOP can be used as complementary tools. It is expected that the analysis results and conclusions can be adapted to other AMS as well.

This paper focuses on the use of safety barrier analysis, during the design phase of a vessel powered by cryogenic hydrogen, to identify possible weaknesses in the architecture. Barrier analysis can be used to evaluate a series of scenarios that have been identified in the industry as critical. The performance evaluation of such barriers in a specific scenario can lead to either the approval of the design, if a safety threshold is met, or the inclusion of additional barriers to mitigate risk even further. By conducting a structured analysis, it is possible to identify key barriers that need to be included in the system, intended both as physical barriers (sensors, cold box) and as administrative barriers (checklist, operator training). The method chosen for this study is the Barrier and Operational Risk Analysis (BORA) method. This method, developed for the analysis of hydrocarbon releases, is described in the paper and adapted for the analysis of cryogenic hydrogen releases. A case study is presented using the BORA method, developing the qualitative barrier analysis. The qualitative section of the method can be easily adapted to vessels of different class and size adopting the same storage solution. The barrier analysis provides a general framework to analyze the system and check that the safety requirements defined by the ship operator and maritime certification societies are met.

The chain of accidents, also known as the domino effect, is responsible for several severe accidents in the chemical and process industries. The pool fire is often blamed as one of the primary accidents triggering a domino event. The present study is devoted to analyzing whether the pool fire alone can cause a domino event in processing and storage facilities. Two models, including a solid flame model and computational fluid dynamic model, are applied to simulate the escalation vector caused by a pool fire. The escalation vector probability is calculated using a probit model for a potential domino effect. This study also investigates the possible factors that can cause a domino effect and determine credible accident scenarios. The proposed concept of escalation vector and numerical models are tested using two past accidents. The study estimates the possibility of pool fires alone causing a domino effect. The results of this study show that although the pool fire alone has the ability to cause a domino event, it is unlikely to occur if a safe distance separates the equipment and proper mitigation measures are employed.

Fire is among the most common and devastating accidents in the hydrocarbon production and processing industry. Many efforts have been dedicated to assessing fire accident likelihood; however, most of these studies considered fire probability as spatially distributed, ignoring the time dependence of the fire accident scenario. In this study, a robust and practical model is proposed to analyze fire accident probability in a congested and complex processing area. This model integrates a conditional probability approach – the Bayesian network (BN) - with a time-dependent scenario evolution approach, Stochastic Petri Nets (SPN). The computational fluid dynamics (CFD) tool is used to estimate the time-dependent scenario consequences. The outcome of the model is fire probability as a function of time and location caused by a specific leak rate and leak duration. A case study of fire probability analysis in a Floating Liquified Natural Gas facility (FLNG) is presented. This study demonstrates the importance of the temporal dependency of the fire scenario and the proposed model can serve as the required tool for time-dependent fire probability analysis, further safety measures’ application and system optimization.

Fire remains a serious threat to a floating liquefied natural gas facility. It is of greater concern given the remote locations and limited accessibility of emergency services. This study aims to present a rigorous procedure to study potential accident scenarios in an offshore (floating processing) facility with different ignition source locations and verify the effectiveness of safety measures using computational fluid dynamics code. The uniqueness of the present study is the integration of release, dispersion and fire modeling scenarios, simplifying the fire analysis and increasing its effectiveness from the offshore process system design and analysis perspectives. The first step of the procedure is to identify the range of potential release scenarios and their strength of dispersion in confined and semi-confined spaces. Subsequently, potential fire scenarios are analyzed considering the influence of the location. Computational fluid dynamics models are used to analyze these three steps of the scenarios. Application of the procedure is demonstrated on an offshore facility by analyzing 14 credible scenarios. The ranges of safety measures of these fires are also studied to determine their effectiveness to prevent fires and mitigate their impact. This study provides a simple and efficient way to analyze the impact of key design parameters. In this study, the transition from fire to explosion is not considered and all the environmental factors are assumed to be constants in the simulation.