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This article presents a metocean modelling methodology using a Markov-switching autoregressive model to produce stochastic wind speed and wave height time series, for inclusion in marine risk planning software tools. By generating a large number of stochastic weather series that resemble the variability in key metocean parameters, probabilistic outcomes can be obtained to predict the occurrence of weather windows, delays and subsequent operational durations for specific tasks or offshore construction phases. To cope with the variation in the offshore weather conditions at each project, it is vital that a stochastic weather model is adaptable to seasonal and inter-monthly fluctuations at each site, generating realistic time series to support weather risk assessments. A model selection process is presented for both weather parameters across three locations, and a personnel transfer task is used to contextualise a realistic weather window analysis. Summarising plots demonstrate the validity of the presented methodology and that a small extension improves the adaptability of the approach for sites with strong correlations between wind speed and wave height. It is concluded that the overall methodology can produce suitable wind speed and wave time series for the assessment of marine operations, yet it is recommended that the methodology is applied to other sites and operations, to determine the method’s adaptability to a wide range of offshore locations.

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Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the results of industrial simulation codes, or detect specific phenomena or anomalies. This paper focuses on data originating from expensive simulation codes to take into account the realistic case where only a limited quantity of information about the studied process is available. A detection methodology based on different features, such as h-mode depth or the dynamic time warping, is proposed to evaluate the outlyingness both in the magnitude and shape senses. Theoretical examples are used to identify pertinent feature combinations and showcase the quality of the detection method with respect to state-of-the-art methodologies of detection. Finally, we show the practical interest of the method in an industrial context thanks to a nuclear thermal-hydraulic use case and how it can serve as a tool to perform sensitivity analysis on functional data.

Nuclear Reactor Pressure Vessel (RPV) integrity is a major issue concerning plant safety and this component is one of the few within a Pressurized Water Reactor (PWR) whose replacement is not considered as feasible. To ensure that adequate margins against failure are maintained throughout the vessel service life, research engineers have developed and applied computational tools to study and assess the probability of pressure vessel failure during operating and postulated loads. The Materials Ageing Institute (MAI) sponsored a benchmark study to compare the results from software developed in France, Japan and the United States to compute the probability of flaw initiation in reactor pressure vessels. This benchmark study was performed to assess the similarities and differences in the software and to identify the sources of any differences that were found. Participants in this work included researchers from EDF in France, CRIEPI in Japan and EPRI in the United States, with each organization using the probabilistic software tool that had been developed in their country. An incremental approach, beginning with deterministic comparisons and ending by assessing Conditional Probability of crack Initiation (CPI), provided confirmation of the good agreement between the results obtained from the software used in this benchmark study. This conclusion strengthens the confidence in these probabilistic fracture mechanics tools and improves understanding of the fundamental computational procedures and algorithms.