Dynamic quantitative risk assessment of LNG bunkering SIMOPs based on Bayesian network

Liquified natural gas(LNG)bunkering simultaneous operations(SIMOPs)refers to the operations(such as cargo operations,port activities and ship maintenance)occurring around LNG bunkering.SIMOPs pose new risks to LNG bunkering,because the operations are dynamically interlocked in which the occurrence probabilities of potential consequences change at different times due to commencement or completion of specific SIMOP events.However,traditional static risk assessment approaches are not able to take the dynamic nature of these new risks into account.This article proposes a dynamic quantitative risk as-sessment(DQRA)methodology based on the Bayesian network(BN)to develop better understanding of dynamic risks of LNG bunkering SIMOPs.The methodology is demonstrated and evaluated through a truck-to-ship LNG bunkering case study.The results and discussion of the case study validate the utility of the proposed methodology and demonstrate that BNs are efficient in performing the probability calcu-lations and are flexible in conducting causal diagnosis.The main innovation of this work is realizing the quantification of risks at different times,which reflects the most essential time-changing characteristics of risks associated with LNG bunkering SIMOPs.

Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes

Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.