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Division Spotlight
Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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Latest News
DOE issues final RFQ for WIPP clean energy initiative
The Department of Energy’s Office of Environmental Management has issued a request for qualifications for interested parties and prospective offerors looking to enter into a realty agreement for carbon-pollution-free electricity (CFE) projects at the department’s Waste Isolation Pilot Plant site in southeastern New Mexico.
Michael Khazen, Arie Dubi
Nuclear Science and Engineering | Volume 141 | Number 3 | July 2002 | Pages 272-287
Technical Paper | doi.org/10.13182/NSE02-A2282
Articles are hosted by Taylor and Francis Online.
Estimation of the probabilities of rare events with significant consequences, e.g., disasters, is one of the most difficult problems in Monte Carlo applications to systems engineering and reliability. The Bernoulli-type estimator used in analog Monte Carlo is characterized by extremely high variance when applied to the estimation of rare events. Variance reduction methods are, therefore, of importance in this field.The present work suggests a parametric nonanalog probability measure based on the superposition of transition biasing and forced events biasing. The cluster-event model is developed providing an effective and reliable approximation for the second moment and the benefit along with a methodology of selecting near-optimal biasing parameters. Numerical examples show a considerable benefit when the method is applied to problems of particular difficulty for the analog Monte Carlo method.The suggested model is applicable for reliability assessment of stochastic networks of complicated topology and high redundancy with component-level repair (i.e., repair applied to an individual failed component while the system is operational).