M. Hari Prasad

Risk management of NPPs using risk monitors

In nuclear power plants (NPPs), safety is the major concern. Probabilistic safety assessment (PSA) has become a key tool as on today to identify and understand NPP vulnerabilities. PSA models have been successfully employed during design evaluation to assess weak links and carry out design modifications to improve system reliability and safety. As a result of the availability of these PSA studies, one can make use of them to enhance plant safety and to operate the plants in the most efficient manner. This necessitates development of software tools like living PSA, risk monitor etc. Risk monitor is a PC based tool, which computes the real time safety level based on the actual status of systems and components and assists plant personnel and regulatory authorities to manage day-to-day activities and can provide solutions to various regulatory decision making issues. This paper discusses various modules and data flow diagrams of risk monitor and also discusses how common cause failures are treated in risk monitor. In risk monitor, risk is subjected to the change in the state of the system, which in turn depends on the state of the components and these are explained with a case study.

Reliability estimation of passive systems using fuzzy fault tree approach

In general, a power plant (nuclear, thermal, chemical etc.) consists of operating and emergency safety systems. These systems vary from very complex to simple systems. A system normally consists of active components and passive components. The failure of any operating system will lead to a change in the state of the plant. The availability of the plant depends on the successful operation of the operating systems and the operation of the components in the systems. In order to ensure the availability of the plant reliability of the systems/components should be ensured. In recent years most of the advanced nuclear reactors implement passive systems, aimed at improved safety and availability. In the traditional reliability analysis of passive systems the failure probability is estimated based on the actual components present in the system and their corresponding failure data information. However, the passive system may fail to fulfill its mission not only because of a consequence of classical mechanical failure of component (passive or active) of the passive system, but also due to the deviation from expected behavior due to physical phenomena mainly related to thermal hydraulic (called as virtual component, VC). Hence, one should consider the failure probability of the VC in the analysis. In this paper a methodology for performing passive system reliability, which combines the actual component failures and the failure of VC, has been proposed based on fuzzy fault tree approach. This methodology will eliminate the simulation based approach that is being adopted in the present day passive system reliability analysis. The methodology has been demonstrated with a case study on passive decay heat removal system of a typical nuclear power plant.

Reliability studies of decay heat removal system

Passive systems have been implemented in most of the advanced reactors in order to improve the availability of the plant. In this paper the methodology used for performing the passive system reliability analysis has been discussed. A case study on passive decay heat removal system of large sized PHWRs has also been discussed. Based on the thermal hydraulic analysis failure points have been generated and the failure surface has been developed. Finally by using the Monte Carlo simulation technique failure probability has been estimated.

Use of Bayesian Networks for System Reliability Assessment

Probabilistic Safety Assessment (PSA) is a technique to quantify the risk associated with complex systems like Nuclear Power Plants (NPPs), chemical industries, aerospace industry, etc. PSA aims at identifying the possible undesirable scenarios that could occur in a plant, along with the likelihood of their occurrence and the consequences associated with them. PSA of NPPs is generally performed through Fault Tree (FT) and Event Tree (ET) approach. FTs are used to evaluate the unavailability or frequency of failure of various systems in the plant, especially those that are safety critical. Some of the limitations of FTs and ETs are consideration of constant failure/repair data for components. Also, the dependency between the component failures is handled in a very conservative manner using beta factor, alpha factors, etc. Recently, the trend is shifting toward the development of Bayesian Network (BN) model of FTs. BNs are directed acyclic graphs and work on the principles of probability theory. The paper highlights how to develop BN from FT and how it can be used to develop a BN model of the FT of Isolation Condenser (IC) of the advanced reactor and incorporate the system component indicator status into the BN. The indicator status would act like evidence to the basic events, thus updating their probabilities.

Approach for Probabilistic Safety Assessment of Accelerator

Probabilistic Safety Assessment revolves around identifying all the potential initiating events, developing the accident scenarios and analyzing the consequence of accident sequence. In case of accelerator, reference initiating event list is not available, which needs to be prepared based on precursor review, engineering evaluation and operating experience. Defining the consequence or risk from accelerator posed yet another major challenge. Risk in terms of absorbed dose has been proposed as one of the measure, which puts forth the hurdle of deciding the Frequency Vs Dose curve for a typical accelerator facility. There are some documents such as NUREG 1860, which proposes an F-C curve in terms of radiation dose under a techno neutral framework for consequence assessment for nuclear facilities. The paper discusses these challenges and framework developed for conducting probabilistic safety assessment of accelerators.

Review of R&D activities in Level-1 Probabilistic Safety Assessment and aging studies

This paper deals with the research and development activities in Level-1 Probabilistic Safety Assessment (PSA) and aging studies . Earlier, PSA models have been successfully employed during design evaluation in order to assess weak links and carry out design modifications to improve system reliability and safety. Now,studies are directed towards applying PSA in various decision making issues concerned with plant operations and safety regulations. This necessitated the development of software tools like Risk Monitor, Diagnostic System etc. in order to enable faster risk evaluation whenever the plant undergoes any design modifications as well as changes in component status and parameters or plant/system configuration. Also, research has been focussed into improving the PSA models and component failure data to be used. Paper highlights the research activities in reliability prediction of hardware and software components, so that realistic estimate can be used. Ageing studies play a vital role in equipment qualification as well as in life extension of components employed in plants. The paper highlights the experimental facilities available and their contribution in effective ageing assessment of Control and Instrumentation components used in Nuclear Power Plants (NPP).

Bayesian analysis for parameter estimation for use in PSA - a case study

One of the approaches existing for parameter estimation is Bayesian method. In this methodology, based on prior plant experience or industry experience prior distribution is assigned to the parameter to be estimated. Based on the evidence during the period of observation, the analysts prior belief about the parameter is updated using Bayes theorem. In this paper general methodology for carrying out the Bayesian updation has been discussed. Both conjugate prior and non conjugate prior have been considered in the analysis. Kalmogorov-Smirnov hypothesis test has been performed for checking the goodness of fit of the distributions. A case study has been discussed.