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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Feinstein Institutes to research novel radiation countermeasure
The Feinstein Institutes for Medical Research, home of the research institutes of New York’s Northwell Health, announced it has received a five-year, $2.9 million grant from the National Institutes of Health to investigate the potential of human ghrelin, a naturally occurring hormone, as a medical countermeasure against radiation-induced gastrointestinal syndrome (GI-ARS).
A. Chandrakar, A. K. Nayak, Vinod Gopika
Nuclear Technology | Volume 194 | Number 1 | April 2016 | Pages 39-60
Technical Paper | doi.org/10.13182/NT15-80
Articles are hosted by Taylor and Francis Online.
Research in the field of passive system reliability analysis is garnering sharp interest in the nuclear community. Passive systems are being utilized extensively in current- and future-generation reactors for their normal operations as well as for safety critical operations during any accidental conditions. In this paper, we present a methodology called Analysis of Passive System ReliAbility Plus (APSRA+) for evaluating reliability of passive systems. This methodology is an improved version of the existing APSRA methodology. The methodology has been applied to the passive isolation condenser system (ICS) of the AHWR (Advanced Heavy Water Reactor). With the help of the APSRA+ methodology, the probability of the passive ICS failing to maintain the clad temperature under 400°C is estimated to be of the order 1×10−10.
Important features of APSRA+ are the following. First, it provides an integrated dynamic reliability method for the consistent treatment of dynamic failure characteristics such as multistate failure, fault increment, and time-dependent failure rate of components of passive systems. Second, this methodology overcomes the issue of process parameter treatment by just the probability density function or by root cause analysis, by segregating the parameters into dependent and independent process parameters and then giving a proper treatment to each of them separately. Third, the methodology treats the model uncertainties and independent process parameter variations in a consistent manner.
In APSRA+, the important parameters affecting the passive system under consideration are identified using sensitivity analysis. To evaluate the system performance, a best-estimate system code is used with due consideration of the uncertainties in empirical models. A failure surface is generated by varying all the identified important parameters; variation from the nominal values of these parameters affects the system performance significantly. These parameters are then segregated into dependent and independent categories. For dependent parameters, it is attributed that the variations of process parameters are mainly due to malfunction of mechanical components or control systems, and hence, root cause analysis is performed. The probability of these dependent parameter variations is estimated using a dynamic reliability methodology based on Monte Carlo simulation. The dynamic failure characteristics of the identified causal component/system are accounted for in calculating these probabilities. For the treatment of independent process parameters, using APSRA+ suggests adopting and integrating classical data-fitting techniques or mathematical models. In the next steps, a response surface-based metamodel is formulated using the generated failure points. The probability of the system being in the failure zone is estimated by sampling and analyzing a sufficiently large number of samples for all the dependent and independent process parameters based on the probability of variations of these parameters, which were estimated using dynamic reliability methodology.
