ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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!
Latest Magazine Issues
Apr 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
May 2025
Nuclear Technology
April 2025
Fusion Science and Technology
Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
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.
