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Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
2021 ANS Virtual Annual Meeting
June 14–16, 2021
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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Extraterrestrial Pu found in the ocean sheds light on cosmic events
Traces of freshly made plutonium and radioactive iron recovered from the bottom of the Pacific Ocean are contributing to an understanding of how heavier elements are created from exploding stars and other cosmic events, according to a National Public Radio report.
Y. Richet, G. Caplin, J. Crevel, D. Ginsbourger, V. Picheny
Nuclear Science and Engineering | Volume 175 | Number 1 | September 2013 | Pages 1-18
Technical Paper | dx.doi.org/10.13182/NSE11-116
Articles are hosted by Taylor and Francis Online.
Nuclear criticality safety assessment often requires groupwise Monte Carlo simulations of k-effective in order to check subcriticality of the system of interest. A typical task to be performed by safety assessors is hence to find the worst combination of input parameters of the criticality Monte Carlo code (i.e., leading to maximum reactivity) over the whole operating range. Then, checking subcriticality can be done by solving a maximization problem where the input-output map defined by the Monte Carlo code expectation (or an upper quantile) stands for the objective function or “parametric” model. This straightforward view of criticality parametric calculations complies with recent works in Design of Computer Experiments, an active research field in applied statistics. This framework provides a robust support to enhance and consolidate good practices in criticality safety assessment. Indeed, supplementing the standard “expert-driven” assessment by a suitable optimization algorithm may be helpful to increase the reliability of the whole process and the robustness of its conclusions. Such a new safety practice is intended to rely on both well-suited mathematical tools (compliant optimization algorithms) and computing infrastructure (a flexible grid-computing environment).