عدم قطعیت و تجزیه و تحلیل حساسیت برای آزمون یکپارچه سازی مدل عدم دسترسی وابسته به سن و تعمیر و نگهداری

The interest in operational lifetime extension of the existing nuclear power plants is growing. Consequently, plants life management programs, considering safety components ageing, are being developed and employed. Ageing represents a gradual degradation of the physical properties and functional performance of different components consequently implying their reduced availability. Analyses, which are being made in the direction of nuclear power plants lifetime extension are based upon components ageing management programs. On the other side, the large uncertainties of the ageing parameters as well as the uncertainties associated with most of the reliability data collections are widely acknowledged. This paper addresses the uncertainty and sensitivity analyses conducted utilizing a previously developed age-dependent unavailability model, integrating effects of test and maintenance activities, for a selected stand-by safety system in a nuclear power plant. The most important problem is the lack of data concerning the effects of ageing as well as the relatively high uncertainty associated to these data, which would correspond to more detailed modelling of ageing. A standard Monte Carlo simulation was coded for the purpose of this paper and utilized in the process of assessment of the component ageing parameters uncertainty propagation on system level. The obtained results from the uncertainty analysis indicate the extent to which the uncertainty of the selected component ageing data set influences the performed unavailability calculations on system level, as well as they present sensitivity insights on the equipment. Sensitivity analyses were additionally conducted. The obtained results indicate sensitivity insights associated to the coded Monte Carlo simulation itself as well as component ageing effects sensitivity judgements related to the selected system unavailability calculation.

The unavailability reduction of safety systems and consequently the nuclear power plant (NPP) itself, by utilizing the probabilistic safety assessment (PSA) methodology, is one of the prime goals in the nuclear industry. Thus, improving NPP safety is achievable through improvement of the reliability and availability of its components, which, on a deeper resolution level, is a function of their age throughout plant operational life (Martorell et al., 1999). Ageing represents a gradual degradation of the physical properties and functional performance of different components consequently implying their reduced availability. Components ageing depend strongly on the test and maintenance (T&M) activities they are subjected to. An ever-increasing number of NPPs and their safety-related equipment are approaching their initially intended lifetime deadline. Therefore, the equipment ageing phenomena, seen as a safety-related issue in the nuclear industry, is being paid a great deal of importance in the last two decades. It is reasonable to expect that the ageing phenomena, exhibited as availability and reliability reduction of specific SSCs, may impact the general plant safety. The loss or even a functional capability reduction of some of the most risk-important SSCs can cause some of the multiple levels of protection, defined by the defence-in-depth strategy to deteriorate and by that reduce plant safety. In order to maintain the originally defined NPP safety margins, the ageing-related impact on it must be properly addressed and managed (IAEA, 1990). A lot of scientific work has been done on the subject of studying the effects of ageing NPP equipment. Studies on degradation of various safety-related equipment such as different containment and concrete structures, piping, instrumentation and control equipment, different stainless steel components, etc., due to ageing-related effects as well as methods for assessment of ageing and ageing management programs are just a part of wide set of researches and studies associated to the NPP equipment ageing (Blahoianu et al., 2011, Cicero et al., 2009, Hashemian, 2010, Kobayashi, 2002, Michel et al., 2001, Naus et al., 1996, Naus et al., 1999, Scott et al., 1992, Vesely and Walford, 1988, Volkanovski, 2011 and Wang et al., 2010). For that purpose, NPPs equipment ageing programs were commenced by the US Nuclear Regulatory Commission (USNRC). One of them was the Nuclear Plant Aging Research (NPAR) Program (USNRC, 1991). The NPAR program was conducted by the Office of Research of Nuclear Regulatory Commission (Vesely, 1992). The explicit consideration of the risk effects of ageing, by allowing component failure data to be evaluated for ageing effects and associated risk implications, has been an important feature of the NPAR. By the explicit consideration of the implications the component ageing might have on plant safety, different ageing contributors can be prioritized respective to their risk importance. Ageing of single components and simultaneous ageing of multiple components exhibited in data can be and should be evaluated for their risk effects. Since the risk effects of ageing are not necessarily additive, the risk effects of ageing of a single component can be insignificant, but the same ageing exhibited by several components can be highly risk significant. The risk significant ageing effects exhibited in data are of high priority and their causes need to be evaluated to assure that research programs and ageing management programs focus on these causes. On the other hand, the lack of equipment ageing data as well as the uncertainty associated with them is widely acknowledged problems. Ageing data uncertainty may implicate under- and/or overestimation of the unavailability of a system in the process of risk-informed decision-making (RIDM), e.g. risk-informed T&M optimization (Kancev and Cepin, 2011a and Kancev and Cepin, 2011b). Consequently, the need of incorporating ageing data uncertainties arises in the context of the mentioned RIDM process, as it is important for the decision-maker to be able to estimate how uncertain the results are and how the uncertainty associated with these ageing data is propagated in the model. Most of the work encountered in the literature on risk-informed optimization problems does not consider ageing data uncertainty analysis. Typical uncertainties to be considered are different parameter and model uncertainties associated with system design, which define the system reliability allocation (Marseguerra et al., 2005, Martorell et al., 2007, Painton and Campbell, 1995, Rocco et al., 2003 and Volkanovski and Cepin, 2011) as well as uncertainties associated with T&M activities that govern the system availability and maintainability characteristics (Bunea and Bedford, 2002, Marseguerra et al., 2004a, Marseguerra et al., 2004b and Rocco et al., 2000). This paper addresses the consideration of ageing data uncertainties in an age-dependent unavailability model. This model, which was previously developed (Kancev and Cepin, 2011a) and substantially improved (Kancev and Cepin, 2011b), was utilized herein in its improved form. The model presents an analytical approach for calculating system unavailability with simultaneous consideration of component ageing, testing strategy and effects of T&M activities. The analytical system unavailability is derived as a function of the surveillance test interval (STI) of the case study equipment. A standard safety system of a commercial nuclear power plant is selected as a case study. The objective of the work is to assess the implications of the selected ageing data uncertainty on the system unavailability calculations. For that purpose, a Monte Carlo (MC) simulation is used in order to study and assess uncertainty propagation on system level. Given the analytical system unavailability model is a function of the test interval, two specific cases are considered and compared. In the first case, the system unavailability is calculated for the test interval specified in the surveillance requirements (SRs), which are defined within the technical specifications (TSs) associated to the studied case study system. The second case comprises system unavailability calculation for a test interval derived as an optimal one in the course of a previous study (Kancev and Cepin, 2011b). Probability density functions (PDFs) of the mean system unavailability for these two relevant cases are calculated and compared. The results gained are indicating higher uncertainty impact on the calculated system unavailability in the first case. Additionally, three different sensitivity analyses are performed. The results obtained from the sensitivity analyses identify sensitivity insights related to the application of the MC simulation code itself as well as sensitivity understandings regarding the ageing effects on the case study equipment in the process of calculating system unavailability.

The impact of the component ageing data uncertainties on system unavailability calculations, using a previously developed analytical unavailability model, is studied in this paper. The main feature of this model is calculation of system unavailability with simultaneous consideration of component ageing, testing strategy and effects of T&M activities. Uncertainty and sensitivity analyses are carried out and the obtained results and gained insights are discussed. Due to the lack of component ageing data and the large uncertainties associated with the existing ageing databases, there is a need for the decision-maker during the risk-informed decision process to be able to estimate how uncertain the results are and how the uncertainty associated with these ageing data is propagated in the model. A standard stand-by safety system, the HPSIS, was used as a case study. A MC simulation-based computer code, developed for the purpose of this paper, is used to study the component ageing data uncertainty propagation on system level. The uncertainty analysis shows that the uncertainty impact is growing with the extension of the surveillance test interval Ti. This conclusion is expected in general. However, for the analytical unavailability model utilized herein, the uncertainty increase with extension of Ti is a direct consequence of its non-linearity. For mathematical models linear in time, the absolute uncertainty is time-independent. By comparing the probability density functions of the mean system unavailability calculated for two specific surveillance test intervals, Ti_TS specified by the TS and the optimal one – Ti_opt, it is found that the impact of the uncertainty on the results is larger for the Qsys(Ti_TS) case. Complementary to the uncertainty analysis, sensitivity analyses are carried out as well. By studying the dependency of the mean system unavailability Qsys on the number of trials N per evaluation in the MC simulation process, it is found that calculated Qsys converges for a relatively small N. Sensitivity analysis considering two ageing threshold databases is conducted. Results show that the variability of the calculated system unavailability from the upper and lower ageing threshold is substantially increasing by extension of Ti. At the end, sensitivity calculations for specific BEs, modelling MDPs and MOVs unavailabilities, are performed. It is found that along with the quantitative impact the component ageing has on Qsys, it also has a qualitative impact. For smaller Ti s the top event sensitivity is larger in regard to selected MOVs than to the MDPs. The two MOVs considered herein were selected on the basis of their risk importance, directed by the RIF and RDF. These sensitivity analysis results show that as Ti increases, a qualitative shifting is observable, i.e. the sensitivity in regard to the selected MOVs is decreasing and becomes substantially smaller than the sensitivity regarding the MDPs, which is rising.