[en] Highlights: • Core Damage Frequency and Large Early Release Frequency. • Multi –Unit Risk Metrics. • Aggregation of CDF of NPP through Mean Values. • Aggregation of CDF of NPP as Random Variable. - Abstract: The nuclear generating sites around the world are mostly twin unit and multi-unit sites. The PSA risk metrics Core Damage Frequency (CDF) and Large Early Release Frequency (LERF) currently are based on per reactor reference. The models for level 1 and level 2 PSA have been developed based on single unit. The Fukushima accident has spawned the need to address the issue of site base risk metrics, Site Core Damage Frequency (SCDF) and Site Large Early Release Frequency (SLERF), on the site years rather than reactor years. It is required to develop a holistic framework for risk assessment of a site. In the context of current study, the holistic framework refers to integration of risk from all units, dependencies due to external events and operation time of individual units. There is currently no general consensus on how to arrive at site-specific risk metrics. Some documents provide suggestions for site CDF and site LERF. This paper proposes a new method of aggregation of risk metric from the consideration of operating time of individual units under certain assumptions with a purpose to provide a new conceptual aspect for multi-unit PSA. The result of a case study on hypothetical data shows that site level CDF is not sum of CDF of all units but around 18% higher than unit level CDF. When the CDF is considered to be a random variable then, the new methodology produces site CDF as 50% higher than single unit CDF. These two approaches have been detailed in the paper. For a general data set of CDF for individual units, site CDF would more than individual unit CDF however, it would not be multiples of a single unit value.

[en] Goals: – Develop the fundamental scientific basis to understand, predict, and measure changes in materials and systems, structures and components (SSCs) as they age in environments; – Apply this knowledge to develop and demonstrate methods and technologies that support safe and economical long-term operation of existing reactors; – Research new technologies that enhance plant performance, economics, and safety.

[en] This article presents the INES scale and shows how the gravity of a reactor accident is assessed. The INES scale is made of 7 levels: 1 - anomaly, 2 - incident, 3 - severe incident, 4 - accident with local consequences, 5 - accident with extended consequences, 6 - severe accident, and 7 - major accident. Each level is illustrated by a real accident. Only 2 accidents ranked at the highest level (7): Tchernobyl (Ukraine - 1986) and Fukushima Daiichi (Japan - 2011), they were characterized by a complete meltdown of the reactor core. A partial meltdown occurred for the following reactor accidents: Three Mile Island (USA - 1979), Saint Laurent des Eaux (France - 1969 and 1980), Lucens (Switzerland - 1969), Chapelcross (UK - 1967), Windscale (UK - 1957), EBR-1 (US - 1955) and NRX (Canada - 1952). A quick methodology to assess the gravity of an accident is to estimate the quantity of equivalent radioactivity released in the atmosphere, for instance the equivalent of a few thousands Tera decay/second of iodine 131 corresponds to the level 5. The number of people irradiated is also an important parameter as well as the radiation exposure, for instance for an individual dose over 200 mSv, involving 100 people or more corresponds to a level 5 event, 10 people or more to a level 4 event and less than 10 people to a level 3 event. (A.C.)

[en] The objectives of DEC analysis can be summarized as follows: (c) DEC analyses are used to confirm that features, credited for DEC, have the requested performances to meet their relevant safety objectives, in addition to DBA studies: (i) No core melting such as to ensure prevention of core melting for DEC-A. (ii) Protection actions that are limited in terms of lengths of time and areas of application need to be sufficient to protect people and the environment, this meaning limitation of radiological consequences in DEC-B. (d) In particular, the demonstration needs to meet the following requirements: (i) DEC-A conditions are considered in emergency operating procedures (with other specific procedures or guidelines when applicable). DEC-B conditions need to be considered by SAM guidelines (with other specific procedures or guidelines when applicable). (ii) Any equipment credited in a DEC analysis needs to be adequately qualified to perform its safety functions in the environmental conditions resulting from this DEC situation. (iii) SSCs that are necessary to meet the safety requirements in DEC analyses need to be considered as items important to safety and to be safety classified accordingly.

[en] Highlights: • Limited experimental evidence affects prediction accuracy of severe accident progression. • Modeling consistency and proper understanding of uncertainties are critical issues. • Main paper objectives include the following issues: • Importance of identifying dominant core meltdown phenomena, • Consistency between models and inherent randomness of underlying physics/chemistry. The complexity of phenomena occurring during severe accidents in nuclear reactors, combined with a limited amount of experimental evidence, do not allow for formulating detailed reliable models or making highly accurate predictions of accident progression. Thus, the modeling consistency and a proper understanding of the uncertainties associated with the results of any computer simulations, including those caused by the imperfection and inherent limitations of the available experimental data used in model validation, are critical for improving accident mitigation capabilities and for enhancing the safety of current and future generations of nuclear reactors. The objective of this paper is to give an overview of selected issues illustrating the importance of: (a) identifying the dominant phenomena governing the progression of core meltdown accidents, and (b) formulating models which are consistent with our understanding of the underlying physics and chemistry and of the increasing level of randomness as the accident progresses. The results used as examples, in particular those pertaining to the research performed by the authors and their collaborators, have been obtained over past several years and documented in a several reports (in particular, in the USNRC NUREG series), but have never been included in copy-righted publications. The sources of any other experimental data used in the discussion of specific coupled experimental/modeling issues (typically, also reports of various agencies or labs) are clearly identified in the text. Since some of the examples include recent unpublished results of computer simulations performed using updated versions of the models which have already been published in other journals, only brief information about such models is presently shown, assuming that the reader can find details in the corresponding references.

No abstract available

[en] The phenomenon and mechanism of FCI (Fuel Coolant Interaction) has been widely studied around the world in the past few decades. A series of experiments were performed and several FCI models were developed on the basis of these experiments. However, there are still large uncertainties in the models of FCI and limitations to predict FCI process, especially the reactor scale process. To study the mechanism of FCI, a new FCI experimental facility was designed and further experiments were performed by Shanghai Jiao Tong University, China. The photography of FCI process were obtained by 2 high-speed cameras recording from 2 different directions vertical to each other. Water level changed can also be got from images of FCI process. Pressure peak produced by intense interaction and temperature of coolant are recorded. To discuss the influence of different factors for FCI, numbers of variables are considered in these experiments, including jet material, melt temperature, coolant type, coolant subcooled temperature, release heights, break size and interaction pool size. This paper focuses on the FCI responses for different melt temperatures. Tin, with the melt point of 231.9 C. degrees, was chosen as the melt material since it is possible to acquire a large temperature range of melt from 400 to 1300 C. degrees. The phenomenon responses of FCI process and the particle size responses for different melt temperature are discussed in the paper. (authors)

No abstract available

[en] As one of severe accident mitigation measures gained considerable attention, design concept of retaining the corium inside the nuclear reactor pressure vessel has been successful applied to Loviisa plant and westinghouse plant with passive safety feature. The annular clearance between thermal insulation and reactor pressure vessel is utilized for cooling the vessel wall under hypothetical core melting accidents in In-Vessel Retention (IVR) design. However, the characteristic of core corium is an important problem in determination of heat flux transmitted to vessel wall. Typical test facilities for studying characteristic of corium are studied in this paper, of which the design parameters, experimental methods, system configurations and results are emphatically discussed. The results show that the Rayleigh number tested in experiments is achieved about 2×10¹⁷. Simulant melt material has significant effects on measure and test data. Three-dimensional test model with non-eutectic mixture of several components is recommended for new designed facility. (authors)

[en] The multiple safety systems including high pressure safety injection (HPSI), low pressure safety injection (LPSI), safety injection tank (SIT), and etc. have been designed to protect the core under the accidents in NPPs. If the decay heat from reactor is not removed due to the failure of safety systems under the accidents, the core can be melted. Therefore, the monitoring of reactor internal phenomenon is very important to prevent core meltdown. The deep learning model can be simulated for reactor internal phenomena without knowledge of physical. Deep learning is one of the most active research fields in recent years because computer’s performance has been improved. It has been widely used not only in science but also in various industries such as medicine, advertising, and finance. Deep learning is a technology that applies information processing methods of human brain to machines. The basic structure of Deep learning has a multilayer perceptron (MLP) structure consisting of three or more hidden layers. The MLP is a neural network composed of several nodes and layers. The location of data is as follows: The thermal distribution of core cell, core baffle, bypass, support barrel, down comer, and vessel cylinder. The data was obtained by using MELCOR which is the severe accident analysis code. The operators can maintain the integrity of the reactor when an unexpected severe accident is occurred in the nuclear power plant because the developed model can predict the reactor internal phenomena by the thermal distribution of vessel cylinder.